
# Initializing the random number generator
import random
random.seed(0)
# Ignoring the warnings
import warnings
warnings.filterwarnings("ignore")


# Mounting the google drive to load the data file
from google.colab import drive
drive.mount('/content/drive/')

import os
path = '/content/drive/My Drive/capstone/Dataset'
capstone_path = '/content/drive/My Drive/capstone'
os.chdir(path)

Mounted at /content/drive/


!pip uninstall opencv-python-headless

Found existing installation: opencv-python-headless 4.1.2.30
Uninstalling opencv-python-headless-4.1.2.30:
  Would remove:
    /usr/local/lib/python3.7/dist-packages/cv2/*
    /usr/local/lib/python3.7/dist-packages/opencv_python_headless-4.1.2.30.dist-info/*
  Would not remove (might be manually added):
    /usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtCore-bbdab771.so.4.8.7
    /usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtGui-903938cd.so.4.8.7
    /usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtTest-1183da5d.so.4.8.7
Proceed (y/n)? y
  Successfully uninstalled opencv-python-headless-4.1.2.30


!pip install opencv-python-headless==4.1.2.30

Collecting opencv-python-headless==4.1.2.30
  Using cached opencv_python_headless-4.1.2.30-cp37-cp37m-manylinux1_x86_64.whl (21.8 MB)
Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python-headless==4.1.2.30) (1.19.5)
Installing collected packages: opencv-python-headless
Successfully installed opencv-python-headless-4.1.2.30


!pip install imgaug

Requirement already satisfied: imgaug in /usr/local/lib/python3.7/dist-packages (0.2.9)
Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug) (1.8.0)
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Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug) (0.11.0)


!pip install imagecorruptions

Collecting imagecorruptions
  Downloading imagecorruptions-1.1.2-py3-none-any.whl (2.1 MB)
     |████████████████████████████████| 2.1 MB 8.4 MB/s 
Requirement already satisfied: Pillow>=5.4.1 in /usr/local/lib/python3.7/dist-packages (from imagecorruptions) (7.1.2)
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Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (2.8.2)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (1.3.2)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (0.11.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (1.15.0)
Installing collected packages: imagecorruptions
Successfully installed imagecorruptions-1.1.2


import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import regularizers,optimizers
from keras.models import Sequential
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from keras.layers import Conv2D, MaxPooling2D,Activation
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import pickle              # For saving models to disk and loading saved models
import os
import zipfile
import PIL
import PIL.Image
import pathlib
import cv2
from sklearn.decomposition import PCA
from scipy.stats import stats
import imgaug as ia
ia.seed(1)
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
from imgaug import augmenters as iaa 
import imageio
import re
import shutil


load_from_original = True
compress_required = False ### Change to true when running for first time ###


csv_path = os.path.join(capstone_path,"dataframes")
try:
    df_train_ann = pd.read_csv(os.path.join(csv_path,"df_train_ann.csv"))
    df_test_ann = pd.read_csv(os.path.join(csv_path,"df_test_ann.csv"))
    df_train = pd.read_csv(os.path.join(csv_path,"df_train.csv"))
    df_val = pd.read_csv(os.path.join(csv_path,"df_val.csv"))
    df_car_name = pd.read_csv(os.path.join(csv_path,"df_car_name.csv"))
    load_from_original = False
    print("The data will be loaded from saved files!!!")

except:
    print("The data will be loaded from original files!!!")
    pass

The data will be loaded from saved files!!!


load_from_original

False


### Define various dataframes and constants
BOX_COLOR = (255, 0, 0) # Red
TEXT_COLOR = (0, 0, 0) # White

if load_from_original == True:
    car_img_path = os.path.join(path,'Car Images/')
    car_ann_path = os.path.join(path,'Annotations/')
    df_car_name = pd.read_csv("Car names and make.csv",names=['Car Name'])
    train_img_dir = os.path.join(car_img_path,'Train Images')
    test_img_dir =  os.path.join(car_img_path,'Test Images')
    df_train_ann = pd.read_csv(os.path.join(car_ann_path,'Train Annotations.csv'),names=['filename','xmin','ymin','xmax','ymax','class'])
    df_test_ann = pd.read_csv(os.path.join(car_ann_path,'Test Annotation.csv'),names=['filename','xmin','ymin','xmax','ymax','class'])
    df_train_ann.drop([df_train_ann.index[0]],inplace=True)
    df_test_ann.drop([df_test_ann.index[0]],inplace=True)
    df_train_ann.reset_index(inplace=True,drop=True)
    df_test_ann.reset_index(inplace=True,drop=True)
    ### Create a dictionary of car names and car classes
    count = 0
    car_dict = {}
    for car_name in df_car_name['Car Name'].values:
        if car_name == 'Ram C/V Cargo Van Minivan 2012':
            car_name = 'Ram C-V Cargo Van Minivan 2012'
        car_dict[count+1] = car_name
        count=count+1
    
    ### Split the car names in to car type, model and year
    car_names = list(df_car_name['Car Name'].values)
    car_types = []
    car_year = []
    car_model = []
    for cars in car_names:
        carname = cars.split(' ')
        model_word_length = len(carname) - 3 #Remove 1 each for name od car company, type of car and model year
        car_types.append(carname[len(carname)-2])
        car_year.append(carname[len(carname)-1])
        car_model.append(carname[0])
    df_car_name['Car_Type'] = car_types
    df_car_name['Model'] = car_model
    df_car_name['Year'] = car_year
    df_car_name['Car Name'].replace(to_replace=['Ram C/V Cargo Van Minivan 2012'],value='Ram C-V Cargo Van Minivan 2012',inplace=True)
    
df_car_name.sample(10)


### Below function to get train/test images path from multiple folders
### INPUT - parent folder name
### OUTPUT - a list with full path name for each of the images
def load_images_from_folder(folder):
    file_path = {}
    count = 0
    for folders in os.listdir(folder):
        #print(folders)
        if folders == '.DS_Store':
            pass
        else:
            foldername = os.path.join(folder,folders)
            #print(foldername)
            for filename in os.listdir(foldername):
                count = count+1
                filepath = os.path.join(foldername,filename)
                file_path[filename]=filepath
                #print(filename)
    return file_path


### Below function to do PCA transformation of images to reduce dimesionality. 
### INPUT - image and n_components (default is 50)
### OUTPUT - compressed images returned.
def compress_pca(image,PCA_n_component=50):
    img = cv2.imread(image,cv2.COLOR_BGR2RGB)
    try:
        if(len(img.shape) < 3):
            print(len(img.shape))
            print("\n file_path: ",image)
            gray = cv2.imread(image,0)
            blue=red=green=gray
        else:
            red,green,blue = cv2.split(img)
    except:
        Print("Exception encountered")

    try:
        df_blue = blue/255
        df_green = green/255
        df_red = red/255
        pca_b = PCA(n_components=PCA_n_component)
        pca_b.fit(df_blue)
        trans_pca_b = pca_b.transform(df_blue)
        pca_g = PCA(n_components=PCA_n_component)
        pca_g.fit(df_green)
        trans_pca_g = pca_g.transform(df_green)
        pca_r = PCA(n_components=PCA_n_component)
        pca_r.fit(df_red)
        trans_pca_r = pca_r.transform(df_red)
        b_arr = pca_b.inverse_transform(trans_pca_b)
        g_arr = pca_g.inverse_transform(trans_pca_g)
        r_arr = pca_r.inverse_transform(trans_pca_r)
        img_reduced= (cv2.merge((r_arr, g_arr, b_arr)))
    
    except:
        return None

    return img_reduced


### Below function a wrapper function for compressing a list of images using PCA transformation
### INPUT - list of images, path where to store transformed images and n_components for PCA transformation
### OUTPUT - output a successful message if completed the operation successfully else return error
def compress_image(image_list,resize_path,PCA_n_component=50):
    filelist = os.listdir(resize_path)
    for img in image_list:
        #print(img)
        img_path = img.split('/')
        if img_path[-1] in filelist:
            pass
        else:
            im = compress_pca(img)
            #print(img_path[-1])
            #if img_path[-1] == "00001.jpg":
            #    plt.imshow(im)
            r_path = os.path.join(resize_path,img_path[-1])
            #print(r_path)
            if im is not None:
                im = cv2.convertScaleAbs(im, alpha=(255.0))
                if not cv2.imwrite(r_path, im):
                    raise Exception("Could not write image")
                cv2.waitKey()
                cv2.destroyAllWindows()
            else:
                print("image skipped:",img_path[-1])
    print("The list of images compressed in folder: ", resize_path)
    compress_required = False
    return


### Below function will calculate the height and width of images path present in a dataframe
### INPUT - dataframe and column name of image path column
### OUTPUT - height and width list 
def get_image_dims(df,col):
    height = []
    width = []
    for img_path in df[col].values:
        img = cv.imread(img_path)
        height.append(int(img.shape[0]))
        width.append(int(img.shape[1]))
    return height,width


### Below function will plot the training and validation loss and accuracy. 
### INPUT - model history and metrics
### OUTPUT - plot loss and accuracy curves
def plot_curves(history,metrics):
    nrows = 1
    ncols = 2
    fig = plt.figure(figsize = (10,5))
    for idx,key in enumerate(metrics):
        ax = fig.add_subplot(nrows,ncols,idx+1)
        plt.plot(history.history[key])
        plt.plot(history.history['val_{}'.format(key)])
        plt.title('model {}'.format(key))
        plt.ylabel(key)
        plt.xlabel('epoch')
        plt.legend(['train','validation'],loc='upper left')


### Below function will resize images with given size without losing image's aspect ratio and store in the output folder provided
### INPUT - list of images, path where to store transformed images and image size (default 224)
### OUTPUT - None
def resize_image(image_list,resize_path,image_size=224):
    for img in image_list:
        #print(img)
        im = cv2.imread(img,cv2.COLOR_BGR2RGB)
        old_size = im.shape[:2]  # old_size is in (height, width) format
        ratio = float(image_size) / max(old_size)
        new_size = tuple([int(x * ratio) for x in old_size])
        # new_size should be in (width, height) format
        im = cv2.resize(im, (new_size[1], new_size[0]))
        delta_w = image_size - new_size[1]
        delta_h = image_size - new_size[0]
        top, bottom = delta_h // 2, delta_h - (delta_h // 2)
        left, right = delta_w // 2, delta_w - (delta_w // 2)
        color = [0, 0, 0]
        new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
                                    value=color)
        #print(new_im.shape)
        img_path = img.split('\\')
        #print(img_path[-1])
        r_path = os.path.join(resize_path,img_path[-1])
        print(r_path)
        cv2.imwrite(r_path, new_im)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    return

#img_list = list(df_train["Image_Path"])
#compress_image(img_list,"compress_train/")
#img_list = list(df_test["Image_Path"])
#compress_image(img_list,"compress_test/")


# Function to convert bounding box image into DataFrame 
def bounding_boxes_to_df(bounding_boxes_object):

    # Convert Bounding Boxes Object to Array
    bounding_boxes_array = bounding_boxes_object.to_xyxy_array()
    
    # Convert the array into DataFrame
    df_bounding_boxes = pd.DataFrame(bounding_boxes_array, 
                                     columns=['xmin', 'ymin', 'xmax', 'ymax'])
    
    # Return the DataFrame
    return df_bounding_boxes


# Define all the Augmentations we want to apply to the dataset
# We're setting random `n` agumentations to 2. 
image_augmentations = iaa.SomeOf( 2,
    [                                 
    # Scale the Images
    iaa.Affine(scale=(0.5, 1.5)),

    # Rotate the Images
    iaa.Affine(rotate=(-60, 60)),

    # Shift the Image
    iaa.Affine(translate_percent={"x":(-0.3, 0.3),"y":(-0.3, 0.3)}),

    # Flip the Image
    iaa.Fliplr(1),

    # Increase or decrease the brightness
    iaa.Multiply((0.5, 1.5)),

    # Add Gaussian Blur
    iaa.GaussianBlur(sigma=(1.0, 3.0)),
    
    # Add Gaussian Noise
    iaa.AdditiveGaussianNoise(scale=(0.03*255, 0.05*255))

])


### Function to augment images and store in the file path input.
def image_aug(df, images_path, aug_images_path, augmentor, multiple = 3):
    # Fill this DataFrame with image attributes
    augmentations_df = pd.DataFrame(columns=['filename','width','height','class', 'xmin', 'ymin', 'xmax', 'ymax'])
    
    # Group the data by filenames
    grouped_df = df.groupby('filename')

    # Create the directory for all augmentated images
    if not os.path.exists(aug_images_path):
        os.mkdir(aug_images_path)

    for i in range(multiple):
        # Post Fix we add to the each different augmentation of one image
        image_postfix = str(i)
        # Loop to perform the augmentations
        for filename in df['filename'].unique():
            file_name = filename.split('.')
            fname = file_name[0] + image_postfix + file_name[1]
            augmented_path = os.path.join(aug_images_path, fname)
            # Take one image at a time with its information
            single_image = grouped_df.get_group(filename)
            single_image = single_image.reset_index()
            single_image = single_image.drop(['index'], axis=1)   
            # Read the image
            image = imageio.imread(os.path.join(images_path, filename))
            # Get bounding box
            bounding_box_array = single_image.drop(['filename', 'width', 'height',
                                                    'class'], axis=1).values
            # Give the bounding box to imgaug library
            bounding_box = BoundingBoxesOnImage.from_xyxy_array(bounding_box_array, 
                                                                shape=image.shape)
            # Perform random 2 Augmentations
            image_aug, bounding_box_aug = augmentor(image=image, 
                                                    bounding_boxes=bounding_box)
            # Discard the the bounding box going out the image completely   
            bounding_box_aug = bounding_box_aug.remove_out_of_image()
            # Clip the bounding box that are only partially out of th image
            bounding_box_aug = bounding_box_aug.clip_out_of_image()
            # Get rid of the the image if bounding box was discarded  
            if re.findall('Image...', str(bounding_box_aug)) == ['Image([]']:
                pass
            else:
                # Create the augmented image file
                imageio.imwrite(augmented_path, image_aug) 
                # Update the image width and height after augmentation
                info_df = single_image.drop(['xmin', 'ymin', 'xmax', 'ymax'], axis=1)    
                for index, _ in info_df.iterrows():
                    info_df.at[index, 'width'] = image_aug.shape[1]
                    info_df.at[index, 'height'] = image_aug.shape[0]
                # Add the prefix to each image to differentiate if required
                info_df['filename'] = info_df['filename'].apply(lambda x: x + image_postfix + '.jpg')
                # Create the augmented bounding boxes dataframe 
                bounding_box_df = bounding_boxes_to_df(bounding_box_aug)
                # Concatenate the filenames, height, width and bounding boxes 
                aug_df = pd.concat([info_df, bounding_box_df], axis=1)
                # Add all the information to augmentations_df we initialized above
                augmentations_df = pd.concat([augmentations_df, aug_df])            
      
    # Remove index
    augmentations_df = augmentations_df.reset_index()
    augmentations_df = augmentations_df.drop(['index'], axis=1)

    # Return the Dataframe
    return augmentations_df

#image_directory = os.path.join(os.getcwd(),"resize_size")
#augmented_images_df = image_aug(df_img_aug, image_directory, 'aug_images_resize', 
#                                image_augmentations)


def visualize_bbox(img, bbox, class_name, color=BOX_COLOR, thickness=2):
    """Visualizes a single bounding box on the image"""
    x_min, y_min, x_max, y_max = map(int,bbox)
    cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=BOX_COLOR, thickness=thickness)
    ((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)    
    cv2.rectangle(img, (x_min, (y_min - int(0.3 * text_height))), (x_min + text_width, y_min), BOX_COLOR, -1)
    cv2.putText(
        img,
        text=class_name,
        org=(x_min, y_min - int(0.3 * text_height)),
        fontFace=cv2.FONT_HERSHEY_SIMPLEX,
        fontScale=0.35, 
        color=TEXT_COLOR, 
        lineType=cv2.LINE_AA,
    )
    return img


def visualize(image_list, bbox_list, class_list,normalize=False):
    count = 0
    image_final = []
    for image_name in image_list:
        image = cv2.imread(image_name,cv2.COLOR_BGR2RGB)
        img = image.copy()
        h,w,_ = img.shape
        bbox = bbox_list[count]
        if normalize == True:
            bbox[0] = bbox[0]*w
            bbox[1] = bbox[1]*h
            bbox[2] = bbox[2]*w
            bbox[3] = bbox[3]*h
        class_name = class_list[count]
        img = visualize_bbox(img, bbox, str(class_name))
        image_final.append(img)
        count = count + 1
    fig, axs = plt.subplots(int(count), 1,figsize=(70, 70))
    axs = axs.flatten()
    for img,ax in zip(image_final,axs):
        ax.imshow(img)
    plt.show()


# YOLO requires the normalized height and width format.
def YOLO_xywh(xmin, ymin, xmax, ymax, imgw,imgh):
  xmin = float(xmin)
  ymin = float(ymin)
  xmax = float(xmax)
  ymax = float(ymax)
  imgw = float(imgw)
  imgh = float(imgh)
  # print (float(xmin), float(ymin), float(xmax), float(ymax), float(imgw),float(imgh))
  xcen = float((xmin + xmax)) / 2 / imgw
  ycen = float((ymin + ymax)) / 2 / imgh
  w = float((xmax - xmin)) / imgw
  h = float((ymax - ymin)) / imgh
  return xcen, ycen, w, h


df_train_ann


if load_from_original == True:
    train_images_path = load_images_from_folder(train_img_dir)
    test_images_path = load_images_from_folder(test_img_dir)
    df_train_ann['filepath'] = df_train_ann['filename'].apply(lambda x:train_images_path[x])
    df_train_ann['class_name'] = df_train_ann['class'].apply(lambda x:car_dict[int(x)])
    df_train_ann['xmin'] = df_train_ann.xmin.astype(float)
    #df_train_ann['class'] = df_train_ann.'class'.astype(int)
    #ds_train_ann['Bbox'] = ds_train_ann.apply(lambda x:list([x['Start_x'],x['Start_y'],x['End_x'],x['End_y']]),axis=1)
    df_test_ann['filepath'] = df_test_ann['filename'].apply(lambda x:test_images_path[x])
    df_test_ann['xmin'] = df_test_ann.xmin.astype(float)
    #df_test_ann['class'] = df_test_ann.'class'.astype(int)
    df_test_ann['class_name'] = df_test_ann['class'].apply(lambda x:car_dict[int(x)])
    #df_test_ann['Bbox'] = ds_test_ann.apply(lambda x:list([x['Start_x'],x['Start_y'],x['End_x'],x['End_y']]),axis=1)


if load_from_original == True:
    trainh,trainw = get_image_dims(df_train_ann,'filepath')
    df_train_ann['width'] = trainw
    df_train_ann['height'] = trainh
    testh,testw = get_image_dims(df_test_ann,'filepath')
    df_test_ann['width'] = testw
    df_test_ann['height'] = testh
    col_list = ['filename','width','height','class','xmin','xmax','ymin','ymax','class_name','filepath']
    df_train_ann = df_train_ann[col_list]
    df_test_ann = df_test_ann[col_list]


df_train_ann.sample(5)


df_test_ann.sample(5)


from sklearn.model_selection import train_test_split
if load_from_original == True:
    df_train,df_val = train_test_split(df_train_ann,test_size=0.2,random_state=1,stratify=df_train_ann['class'])


df_train.shape

(6515, 10)


df_train.sample(5)


df_val.shape

(1629, 10)


df_val.sample(5)


print(list(df_train['class'].value_counts()))
print(list(df_val['class'].value_counts()))
print(list(df_test_ann['class'].value_counts()))

[54, 39, 38, 38, 38, 38, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 29, 29, 29, 29, 28, 28, 28, 27, 27, 27, 27, 27, 27, 27, 26, 25, 24, 23, 23, 19]
[14, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 5, 5]
[68, 48, 48, 47, 46, 46, 46, 46, 46, 46, 46, 46, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 35, 35, 35, 35, 35, 34, 34, 34, 33, 33, 33, 32, 32, 32, 30, 29, 29, 27, 24]


print(len(list(df_train['class'].value_counts())))
print(len(list(df_val['class'].value_counts())))
print(len(list(df_test_ann['class'].value_counts())))

196
196
196


if load_from_original == True:
    csv_path = os.path.join(capstone_path,"dataframes")
    df_train_ann.to_csv(os.path.join(csv_path,"df_train_ann.csv"),index=None)
    df_test_ann.to_csv(os.path.join(csv_path,"df_test_ann.csv"),index=None)
    df_train.to_csv(os.path.join(csv_path,"df_train.csv"),index=None)
    df_val.to_csv(os.path.join(csv_path,"df_val.csv"),index=None)
    df_car_name.to_csv(os.path.join(csv_path,"df_car_name.csv"),index=None)
    load_from_original = False


df_visualize = df_train_ann[['filepath','class_name','xmin','ymin','xmax','ymax']].sample(10)
df_visualize['Bbox'] = df_visualize.apply(lambda x:list([float(x['xmin']),float(x['ymin']),float(x['xmax']),float(x['ymax'])]),axis=1)
df_visualize.drop(columns=['xmin','ymin','xmax','ymax'],inplace=True)


df_visualize


image_list = list(df_visualize['filepath'])
class_list = list(df_visualize['class_name'])
bbox_list = list(df_visualize['Bbox'])
visualize(image_list,bbox_list,class_list)


if compress_required == True:
    comp_image_path = os.path.join(capstone_path,"compressed_images")
    img_list = list(df_train["filepath"])
    compress_image(img_list,comp_image_path)
    compress_required = True
    comp_image_path = os.path.join(capstone_path,"compressed_val_images")
    img_list = list(df_val["filepath"])
    compress_image(img_list,comp_image_path)
    compress_required = True  
    comp_image_path = os.path.join(capstone_path,"compressed_test_images")
    img_list = list(df_test_ann["filepath"])
    compress_image(img_list,comp_image_path)  
    compress_required = False


!python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

2022-01-30 08:03:18.822503: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
tf.Tensor(-1019.2014, shape=(), dtype=float32)


os.chdir(capstone_path)


%cd tensorflow

/content/drive/My Drive/capstone/tensorflow


#!git clone https://github.com/tensorflow/models.git


%cd ./models/research/

/content/drive/My Drive/capstone/tensorflow/models/research


!protoc object_detection/protos/*.proto --python_out=.


#!git clone https://github.com/cocodataset/cocoapi.git


#%cd cocoapi/PythonAPI


#!make


#!cp -r pycocotools ../../


!pwd

/content/drive/MyDrive/capstone/tensorflow/models/research


#cd workspace/training-yolo/


#cd yolov5

!ls

adversarial_text    deep_speech		       pcl_rl
attention_ocr	    delf		       pycocotools
audioset	    efficient-hrl	       README.md
autoaugment	    lfads		       rebar
build		    lstm_object_detection      seq_flow_lite
cocoapi		    marco		       setup.py
cognitive_planning  nst_blogpost	       slim
cvt_text	    object_detection	       vid2depth
deeplab		    object_detection.egg-info


!pwd

/content/drive/MyDrive/capstone/tensorflow/models/research


!cp object_detection/packages/tf2/setup.py .


#!python setup.py install


!python -m pip install --use-feature=in-tree-build .

Processing /content/drive/MyDrive/capstone/tensorflow/models/research
Requirement already satisfied: avro-python3 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.10.2)
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Building wheels for collected packages: object-detection
  Building wheel for object-detection (setup.py) ... done
  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=21906139 sha256=a8c23bf6ebdd617de5a4aa8ba513d8841d3f193bed80cbbbcba88351852f7a34
  Stored in directory: /tmp/pip-ephem-wheel-cache-jucvrkqa/wheels/3a/7d/30/e990abb534ba8f86da363004cce7f0d15ec0d2ad6a35117be8
Successfully built object-detection
Installing collected packages: object-detection
  Attempting uninstall: object-detection
    Found existing installation: object-detection 0.1
    Uninstalling object-detection-0.1:
      Successfully uninstalled object-detection-0.1
Successfully installed object-detection-0.1


!python object_detection/builders/model_builder_tf2_test.py

Running tests under Python 3.7.12: /usr/bin/python3
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac
2022-01-30 08:05:54.809461: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
W0130 08:05:55.146317 140098475657088 model_builder.py:1100] Building experimental DeepMAC meta-arch. Some features may be omitted.
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.27s
I0130 08:05:55.386464 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.27s
[       OK ] ModelBuilderTF2Test.test_create_center_net_deepmac
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.49s
I0130 08:05:55.877207 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.49s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s
I0130 08:05:56.140501 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.24s
I0130 08:05:56.378103 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.24s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.65s
I0130 08:05:58.032945 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.65s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet
[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
I0130 08:05:58.033852 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
[       OK ] ModelBuilderTF2Test.test_create_experimental_model
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s
I0130 08:05:58.055828 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s
I0130 08:05:58.069534 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s
I0130 08:05:58.083783 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
I0130 08:05:58.176815 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.09s
I0130 08:05:58.264548 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
I0130 08:05:58.364907 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
I0130 08:05:58.460211 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
I0130 08:05:58.554832 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
I0130 08:05:58.585132 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config
I0130 08:05:58.761271 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b0
I0130 08:05:58.761420 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 64
I0130 08:05:58.761470 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 3
I0130 08:05:58.763537 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:05:58.779495 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:05:58.779605 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:05:58.836241 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:05:58.836394 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:05:58.977824 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:05:58.977968 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40
I0130 08:05:59.112708 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40
I0130 08:05:59.112871 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80
I0130 08:05:59.460819 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80
I0130 08:05:59.460990 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112
I0130 08:05:59.666291 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112
I0130 08:05:59.666456 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192
I0130 08:05:59.937867 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192
I0130 08:05:59.938024 140098475657088 efficientnet_model.py:147] round_filter input=320 output=320
I0130 08:06:00.003347 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1280
I0130 08:06:00.030011 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:00.081468 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b1
I0130 08:06:00.081606 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 88
I0130 08:06:00.081659 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 4
I0130 08:06:00.083197 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:06:00.097352 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:06:00.097465 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:06:00.205763 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:06:00.205906 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:06:00.412404 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:06:00.412569 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40
I0130 08:06:00.621731 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40
I0130 08:06:00.621893 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80
I0130 08:06:00.900961 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80
I0130 08:06:00.901120 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112
I0130 08:06:01.176057 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112
I0130 08:06:01.176220 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192
I0130 08:06:01.517135 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192
I0130 08:06:01.517295 140098475657088 efficientnet_model.py:147] round_filter input=320 output=320
I0130 08:06:01.653026 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1280
I0130 08:06:01.681439 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:01.739365 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b2
I0130 08:06:01.739523 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 112
I0130 08:06:01.739573 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 5
I0130 08:06:01.741083 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:06:01.754995 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32
I0130 08:06:01.755105 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:06:01.868809 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16
I0130 08:06:01.868936 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:06:02.075141 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24
I0130 08:06:02.075299 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48
I0130 08:06:02.280961 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48
I0130 08:06:02.281124 140098475657088 efficientnet_model.py:147] round_filter input=80 output=88
I0130 08:06:02.564807 140098475657088 efficientnet_model.py:147] round_filter input=80 output=88
I0130 08:06:02.564962 140098475657088 efficientnet_model.py:147] round_filter input=112 output=120
I0130 08:06:02.846698 140098475657088 efficientnet_model.py:147] round_filter input=112 output=120
I0130 08:06:02.846854 140098475657088 efficientnet_model.py:147] round_filter input=192 output=208
I0130 08:06:03.369178 140098475657088 efficientnet_model.py:147] round_filter input=192 output=208
I0130 08:06:03.369354 140098475657088 efficientnet_model.py:147] round_filter input=320 output=352
I0130 08:06:03.505091 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1408
I0130 08:06:03.533818 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:03.590519 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b3
I0130 08:06:03.590692 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 160
I0130 08:06:03.590770 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 6
I0130 08:06:03.592720 140098475657088 efficientnet_model.py:147] round_filter input=32 output=40
I0130 08:06:03.607010 140098475657088 efficientnet_model.py:147] round_filter input=32 output=40
I0130 08:06:03.607107 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:03.721020 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:03.721172 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32
I0130 08:06:03.930721 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32
I0130 08:06:03.930878 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48
I0130 08:06:04.137856 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48
I0130 08:06:04.138017 140098475657088 efficientnet_model.py:147] round_filter input=80 output=96
I0130 08:06:04.483711 140098475657088 efficientnet_model.py:147] round_filter input=80 output=96
I0130 08:06:04.483870 140098475657088 efficientnet_model.py:147] round_filter input=112 output=136
I0130 08:06:04.830614 140098475657088 efficientnet_model.py:147] round_filter input=112 output=136
I0130 08:06:04.830782 140098475657088 efficientnet_model.py:147] round_filter input=192 output=232
I0130 08:06:05.245160 140098475657088 efficientnet_model.py:147] round_filter input=192 output=232
I0130 08:06:05.245331 140098475657088 efficientnet_model.py:147] round_filter input=320 output=384
I0130 08:06:05.386172 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1536
I0130 08:06:05.412555 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:05.476022 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b4
I0130 08:06:05.476176 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 224
I0130 08:06:05.476250 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7
I0130 08:06:05.477810 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48
I0130 08:06:05.495090 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48
I0130 08:06:05.495225 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:05.607083 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:05.607237 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32
I0130 08:06:05.886535 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32
I0130 08:06:05.886725 140098475657088 efficientnet_model.py:147] round_filter input=40 output=56
I0130 08:06:06.163819 140098475657088 efficientnet_model.py:147] round_filter input=40 output=56
I0130 08:06:06.163996 140098475657088 efficientnet_model.py:147] round_filter input=80 output=112
I0130 08:06:06.579067 140098475657088 efficientnet_model.py:147] round_filter input=80 output=112
I0130 08:06:06.579244 140098475657088 efficientnet_model.py:147] round_filter input=112 output=160
I0130 08:06:07.010339 140098475657088 efficientnet_model.py:147] round_filter input=112 output=160
I0130 08:06:07.010557 140098475657088 efficientnet_model.py:147] round_filter input=192 output=272
I0130 08:06:07.573372 140098475657088 efficientnet_model.py:147] round_filter input=192 output=272
I0130 08:06:07.573561 140098475657088 efficientnet_model.py:147] round_filter input=320 output=448
I0130 08:06:07.952463 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1792
I0130 08:06:07.978668 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:08.051736 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b5
I0130 08:06:08.051899 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 288
I0130 08:06:08.051990 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7
I0130 08:06:08.053609 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48
I0130 08:06:08.067828 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48
I0130 08:06:08.067955 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:08.232801 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24
I0130 08:06:08.232965 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40
I0130 08:06:08.582899 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40
I0130 08:06:08.583068 140098475657088 efficientnet_model.py:147] round_filter input=40 output=64
I0130 08:06:08.935224 140098475657088 efficientnet_model.py:147] round_filter input=40 output=64
I0130 08:06:08.935379 140098475657088 efficientnet_model.py:147] round_filter input=80 output=128
I0130 08:06:09.412943 140098475657088 efficientnet_model.py:147] round_filter input=80 output=128
I0130 08:06:09.413106 140098475657088 efficientnet_model.py:147] round_filter input=112 output=176
I0130 08:06:09.892182 140098475657088 efficientnet_model.py:147] round_filter input=112 output=176
I0130 08:06:09.892339 140098475657088 efficientnet_model.py:147] round_filter input=192 output=304
I0130 08:06:10.524228 140098475657088 efficientnet_model.py:147] round_filter input=192 output=304
I0130 08:06:10.524397 140098475657088 efficientnet_model.py:147] round_filter input=320 output=512
I0130 08:06:10.734781 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2048
I0130 08:06:10.761371 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:10.852889 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b6
I0130 08:06:10.853036 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384
I0130 08:06:10.853110 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8
I0130 08:06:10.854681 140098475657088 efficientnet_model.py:147] round_filter input=32 output=56
I0130 08:06:10.868927 140098475657088 efficientnet_model.py:147] round_filter input=32 output=56
I0130 08:06:10.869042 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32
I0130 08:06:11.036001 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32
I0130 08:06:11.036173 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40
I0130 08:06:11.451443 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40
I0130 08:06:11.451622 140098475657088 efficientnet_model.py:147] round_filter input=40 output=72
I0130 08:06:11.870974 140098475657088 efficientnet_model.py:147] round_filter input=40 output=72
I0130 08:06:11.871149 140098475657088 efficientnet_model.py:147] round_filter input=80 output=144
I0130 08:06:12.418941 140098475657088 efficientnet_model.py:147] round_filter input=80 output=144
I0130 08:06:12.419108 140098475657088 efficientnet_model.py:147] round_filter input=112 output=200
I0130 08:06:13.214609 140098475657088 efficientnet_model.py:147] round_filter input=112 output=200
I0130 08:06:13.214803 140098475657088 efficientnet_model.py:147] round_filter input=192 output=344
I0130 08:06:13.990633 140098475657088 efficientnet_model.py:147] round_filter input=192 output=344
I0130 08:06:13.990813 140098475657088 efficientnet_model.py:147] round_filter input=320 output=576
I0130 08:06:14.198748 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2304
I0130 08:06:14.224623 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
I0130 08:06:14.322106 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b7
I0130 08:06:14.322283 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384
I0130 08:06:14.322356 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8
I0130 08:06:14.323943 140098475657088 efficientnet_model.py:147] round_filter input=32 output=64
I0130 08:06:14.338570 140098475657088 efficientnet_model.py:147] round_filter input=32 output=64
I0130 08:06:14.338687 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32
I0130 08:06:14.561251 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32
I0130 08:06:14.561438 140098475657088 efficientnet_model.py:147] round_filter input=24 output=48
I0130 08:06:15.048639 140098475657088 efficientnet_model.py:147] round_filter input=24 output=48
I0130 08:06:15.048818 140098475657088 efficientnet_model.py:147] round_filter input=40 output=80
I0130 08:06:15.528684 140098475657088 efficientnet_model.py:147] round_filter input=40 output=80
I0130 08:06:15.528866 140098475657088 efficientnet_model.py:147] round_filter input=80 output=160
I0130 08:06:16.230118 140098475657088 efficientnet_model.py:147] round_filter input=80 output=160
I0130 08:06:16.230304 140098475657088 efficientnet_model.py:147] round_filter input=112 output=224
I0130 08:06:16.929373 140098475657088 efficientnet_model.py:147] round_filter input=112 output=224
I0130 08:06:16.929553 140098475657088 efficientnet_model.py:147] round_filter input=192 output=384
I0130 08:06:18.075128 140098475657088 efficientnet_model.py:147] round_filter input=192 output=384
I0130 08:06:18.075303 140098475657088 efficientnet_model.py:147] round_filter input=320 output=640
I0130 08:06:18.362827 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2560
I0130 08:06:18.389021 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 19.92s
I0130 08:06:18.504767 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 19.92s
[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
I0130 08:06:18.512332 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
I0130 08:06:18.514171 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
I0130 08:06:18.514730 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
I0130 08:06:18.516406 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
I0130 08:06:18.517955 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
I0130 08:06:18.518465 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
I0130 08:06:18.519565 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 24 tests in 24.399s

OK (skipped=1)


#%cd ..
#%cd ..


#%cd workspace/training-ssd


ssd_path = os.path.join(capstone_path,"tensorflow/workspace/training-ssd")
os.chdir(ssd_path)


df_train.sample(5)


train_csv = df_train[['filename','width','height','class_name','xmin','ymin','xmax','ymax']]
val_csv = df_val[['filename','width','height','class_name','xmin','ymin','xmax','ymax']]


path_to_pbtext = os.path.join(ssd_path,"annotations/label_map.pbtxt")
path_tfrecord_train =  os.path.join(ssd_path,"annotations/train.record")
path_tfrecord_test =  os.path.join(ssd_path,"annotations/test.record")
path_to_train_images = os.path.join(ssd_path,"images/compressed_images")
path_to_test_images = os.path.join(ssd_path,"images/compressed_test_images")
path_to_train_csv = os.path.join(capstone_path,"train_csv.csv")
path_to_test_csv = os.path.join(capstone_path,"val_csv.csv")


#train_csv.to_csv(path_to_train_csv,index=None)
#val_csv.to_csv(path_to_test_csv,index=None)

train_csv = pd.read_csv(path_to_train_csv)
val_csv = pd.read_csv(path_to_test_csv)


%cd ..

/content/drive/MyDrive/capstone/tensorflow/workspace


%cd ..

/content/drive/MyDrive/capstone/tensorflow


%cd /content/drive/My Drive/capstone/tensorflow/scripts

/content/drive/My Drive/capstone/tensorflow/scripts


#!python generate_tfrecord.py -x ../workspace/training-ssd/images/compressed_images -l ../workspace/training-ssd/annotations/label_map.pbtxt -o ../workspace/training-ssd/annotations/train.record -c ../workspace/training-ssd/images/train_csv.csv


#!python generate_tfrecord.py -x ../workspace/training-ssd/images/compressed_val_images -l ../workspace/training-ssd/annotations/label_map.pbtxt -o ../workspace/training-ssd/annotations/test.record -c ../workspace/training-ssd/images/val_csv.csv

!ls

 Annotations  'Car Images'  'Car names and make.csv'   __MACOSX


%cd /content/drive/My Drive/capstone/tensorflow/workspace/training-ssd/

/content/drive/My Drive/capstone/tensorflow/workspace/training-ssd

!ls

 annotations	       images		  'pre-trained models'
 exported-models       model_main_tf2.py
 exporter_main_v2.py   models


!python model_main_tf2.py --model_dir=models/my_ssd_mobilenet --pipeline_config_path=models/my_ssd_mobilenet/pipeline.config

2022-01-23 12:20:13.368157: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0123 12:20:13.371561 139706229311360 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I0123 12:20:14.230054 139706229311360 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0123 12:20:14.230258 139706229311360 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0123 12:20:14.259042 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['./annotations/train.record']
I0123 12:20:14.674426 139706229311360 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/train.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/train.record']
I0123 12:20:14.674803 139706229311360 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/train.record']
INFO:tensorflow:Number of filenames to read: 1
I0123 12:20:14.674925 139706229311360 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0123 12:20:14.675002 139706229311360 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0123 12:20:14.681763 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0123 12:20:14.709128 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0123 12:20:21.463398 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
W0123 12:20:24.445254 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0123 12:20:26.018429 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2022-01-23 12:20:32.411990: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at multi_device_iterator_ops.cc:789 : NOT_FOUND: Resource AnonymousMultiDeviceIterator/AnonymousMultiDeviceIterator0/N10tensorflow4data12_GLOBAL__N_119MultiDeviceIteratorE does not exist.
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0123 12:21:07.224520 139700694898432 deprecation.py:551] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
INFO:tensorflow:Step 18100 per-step time 1.006s
I0123 12:22:47.513062 139706229311360 model_lib_v2.py:707] Step 18100 per-step time 1.006s
INFO:tensorflow:{'Loss/classification_loss': 0.29952532,
 'Loss/localization_loss': 0.024854204,
 'Loss/regularization_loss': 0.09463329,
 'Loss/total_loss': 0.4190128,
 'learning_rate': 0.058273777}
I0123 12:22:47.513358 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29952532,
 'Loss/localization_loss': 0.024854204,
 'Loss/regularization_loss': 0.09463329,
 'Loss/total_loss': 0.4190128,
 'learning_rate': 0.058273777}
INFO:tensorflow:Step 18200 per-step time 0.644s
I0123 12:23:51.887348 139706229311360 model_lib_v2.py:707] Step 18200 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.42891264,
 'Loss/localization_loss': 0.011297177,
 'Loss/regularization_loss': 0.094637334,
 'Loss/total_loss': 0.53484714,
 'learning_rate': 0.05804527}
I0123 12:23:51.887635 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.42891264,
 'Loss/localization_loss': 0.011297177,
 'Loss/regularization_loss': 0.094637334,
 'Loss/total_loss': 0.53484714,
 'learning_rate': 0.05804527}
INFO:tensorflow:Step 18300 per-step time 0.640s
I0123 12:24:55.929780 139706229311360 model_lib_v2.py:707] Step 18300 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.36802167,
 'Loss/localization_loss': 0.013548903,
 'Loss/regularization_loss': 0.09461092,
 'Loss/total_loss': 0.4761815,
 'learning_rate': 0.05781603}
I0123 12:24:55.930065 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.36802167,
 'Loss/localization_loss': 0.013548903,
 'Loss/regularization_loss': 0.09461092,
 'Loss/total_loss': 0.4761815,
 'learning_rate': 0.05781603}
INFO:tensorflow:Step 18400 per-step time 0.640s
I0123 12:25:59.960031 139706229311360 model_lib_v2.py:707] Step 18400 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.30406886,
 'Loss/localization_loss': 0.012708182,
 'Loss/regularization_loss': 0.0945678,
 'Loss/total_loss': 0.41134486,
 'learning_rate': 0.057586048}
I0123 12:25:59.960329 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30406886,
 'Loss/localization_loss': 0.012708182,
 'Loss/regularization_loss': 0.0945678,
 'Loss/total_loss': 0.41134486,
 'learning_rate': 0.057586048}
INFO:tensorflow:Step 18500 per-step time 0.641s
I0123 12:27:04.089060 139706229311360 model_lib_v2.py:707] Step 18500 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.33735645,
 'Loss/localization_loss': 0.017137185,
 'Loss/regularization_loss': 0.0945202,
 'Loss/total_loss': 0.44901383,
 'learning_rate': 0.05735535}
I0123 12:27:04.089377 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33735645,
 'Loss/localization_loss': 0.017137185,
 'Loss/regularization_loss': 0.0945202,
 'Loss/total_loss': 0.44901383,
 'learning_rate': 0.05735535}
INFO:tensorflow:Step 18600 per-step time 0.642s
I0123 12:28:08.325004 139706229311360 model_lib_v2.py:707] Step 18600 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.38051832,
 'Loss/localization_loss': 0.032725073,
 'Loss/regularization_loss': 0.094505474,
 'Loss/total_loss': 0.50774884,
 'learning_rate': 0.057123937}
I0123 12:28:08.325269 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.38051832,
 'Loss/localization_loss': 0.032725073,
 'Loss/regularization_loss': 0.094505474,
 'Loss/total_loss': 0.50774884,
 'learning_rate': 0.057123937}
INFO:tensorflow:Step 18700 per-step time 0.646s
I0123 12:29:12.927405 139706229311360 model_lib_v2.py:707] Step 18700 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.27072132,
 'Loss/localization_loss': 0.034180954,
 'Loss/regularization_loss': 0.09444116,
 'Loss/total_loss': 0.39934343,
 'learning_rate': 0.056891818}
I0123 12:29:12.927690 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27072132,
 'Loss/localization_loss': 0.034180954,
 'Loss/regularization_loss': 0.09444116,
 'Loss/total_loss': 0.39934343,
 'learning_rate': 0.056891818}
INFO:tensorflow:Step 18800 per-step time 0.645s
I0123 12:30:17.399391 139706229311360 model_lib_v2.py:707] Step 18800 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.42730612,
 'Loss/localization_loss': 0.01078451,
 'Loss/regularization_loss': 0.09441476,
 'Loss/total_loss': 0.5325054,
 'learning_rate': 0.056659}
I0123 12:30:17.399684 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.42730612,
 'Loss/localization_loss': 0.01078451,
 'Loss/regularization_loss': 0.09441476,
 'Loss/total_loss': 0.5325054,
 'learning_rate': 0.056659}
INFO:tensorflow:Step 18900 per-step time 0.638s
I0123 12:31:21.228335 139706229311360 model_lib_v2.py:707] Step 18900 per-step time 0.638s
INFO:tensorflow:{'Loss/classification_loss': 0.33598936,
 'Loss/localization_loss': 0.017465934,
 'Loss/regularization_loss': 0.09439131,
 'Loss/total_loss': 0.44784662,
 'learning_rate': 0.056425504}
I0123 12:31:21.228605 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33598936,
 'Loss/localization_loss': 0.017465934,
 'Loss/regularization_loss': 0.09439131,
 'Loss/total_loss': 0.44784662,
 'learning_rate': 0.056425504}
INFO:tensorflow:Step 19000 per-step time 0.642s
I0123 12:32:25.395089 139706229311360 model_lib_v2.py:707] Step 19000 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.39468116,
 'Loss/localization_loss': 0.015678056,
 'Loss/regularization_loss': 0.094344735,
 'Loss/total_loss': 0.50470394,
 'learning_rate': 0.056191333}
I0123 12:32:25.395357 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.39468116,
 'Loss/localization_loss': 0.015678056,
 'Loss/regularization_loss': 0.094344735,
 'Loss/total_loss': 0.50470394,
 'learning_rate': 0.056191333}
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.492565 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.493672 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.495607 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.496440 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.498345 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.499166 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.501069 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.501894 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.503755 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.504554 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Step 19100 per-step time 0.646s
I0123 12:33:30.007883 139706229311360 model_lib_v2.py:707] Step 19100 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.33192852,
 'Loss/localization_loss': 0.013866619,
 'Loss/regularization_loss': 0.09434637,
 'Loss/total_loss': 0.44014153,
 'learning_rate': 0.055956498}
I0123 12:33:30.008175 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33192852,
 'Loss/localization_loss': 0.013866619,
 'Loss/regularization_loss': 0.09434637,
 'Loss/total_loss': 0.44014153,
 'learning_rate': 0.055956498}
INFO:tensorflow:Step 19200 per-step time 0.648s
I0123 12:34:34.781159 139706229311360 model_lib_v2.py:707] Step 19200 per-step time 0.648s
INFO:tensorflow:{'Loss/classification_loss': 0.44508803,
 'Loss/localization_loss': 0.01622589,
 'Loss/regularization_loss': 0.09434605,
 'Loss/total_loss': 0.55565995,
 'learning_rate': 0.055721}
I0123 12:34:34.781468 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.44508803,
 'Loss/localization_loss': 0.01622589,
 'Loss/regularization_loss': 0.09434605,
 'Loss/total_loss': 0.55565995,
 'learning_rate': 0.055721}
INFO:tensorflow:Step 19300 per-step time 0.645s
I0123 12:35:39.276599 139706229311360 model_lib_v2.py:707] Step 19300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.33601537,
 'Loss/localization_loss': 0.007376912,
 'Loss/regularization_loss': 0.094355084,
 'Loss/total_loss': 0.43774736,
 'learning_rate': 0.05548486}
I0123 12:35:39.276923 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33601537,
 'Loss/localization_loss': 0.007376912,
 'Loss/regularization_loss': 0.094355084,
 'Loss/total_loss': 0.43774736,
 'learning_rate': 0.05548486}
INFO:tensorflow:Step 19400 per-step time 0.640s
I0123 12:36:43.295902 139706229311360 model_lib_v2.py:707] Step 19400 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.29710406,
 'Loss/localization_loss': 0.050312616,
 'Loss/regularization_loss': 0.09431048,
 'Loss/total_loss': 0.44172716,
 'learning_rate': 0.05524808}
I0123 12:36:43.296162 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29710406,
 'Loss/localization_loss': 0.050312616,
 'Loss/regularization_loss': 0.09431048,
 'Loss/total_loss': 0.44172716,
 'learning_rate': 0.05524808}
INFO:tensorflow:Step 19500 per-step time 0.640s
I0123 12:37:47.339904 139706229311360 model_lib_v2.py:707] Step 19500 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.37950346,
 'Loss/localization_loss': 0.013545657,
 'Loss/regularization_loss': 0.09427994,
 'Loss/total_loss': 0.48732907,
 'learning_rate': 0.05501068}
I0123 12:37:47.340184 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.37950346,
 'Loss/localization_loss': 0.013545657,
 'Loss/regularization_loss': 0.09427994,
 'Loss/total_loss': 0.48732907,
 'learning_rate': 0.05501068}
INFO:tensorflow:Step 19600 per-step time 0.643s
I0123 12:38:51.663791 139706229311360 model_lib_v2.py:707] Step 19600 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.37912813,
 'Loss/localization_loss': 0.024981897,
 'Loss/regularization_loss': 0.094227545,
 'Loss/total_loss': 0.49833757,
 'learning_rate': 0.054772656}
I0123 12:38:51.664058 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.37912813,
 'Loss/localization_loss': 0.024981897,
 'Loss/regularization_loss': 0.094227545,
 'Loss/total_loss': 0.49833757,
 'learning_rate': 0.054772656}
INFO:tensorflow:Step 19700 per-step time 0.639s
I0123 12:39:55.608415 139706229311360 model_lib_v2.py:707] Step 19700 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.30867252,
 'Loss/localization_loss': 0.013113073,
 'Loss/regularization_loss': 0.09418946,
 'Loss/total_loss': 0.41597506,
 'learning_rate': 0.05453403}
I0123 12:39:55.608703 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30867252,
 'Loss/localization_loss': 0.013113073,
 'Loss/regularization_loss': 0.09418946,
 'Loss/total_loss': 0.41597506,
 'learning_rate': 0.05453403}
INFO:tensorflow:Step 19800 per-step time 0.645s
I0123 12:41:00.107314 139706229311360 model_lib_v2.py:707] Step 19800 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.3054294,
 'Loss/localization_loss': 0.019898904,
 'Loss/regularization_loss': 0.09416133,
 'Loss/total_loss': 0.41948962,
 'learning_rate': 0.0542948}
I0123 12:41:00.107597 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3054294,
 'Loss/localization_loss': 0.019898904,
 'Loss/regularization_loss': 0.09416133,
 'Loss/total_loss': 0.41948962,
 'learning_rate': 0.0542948}
INFO:tensorflow:Step 19900 per-step time 0.644s
I0123 12:42:04.467776 139706229311360 model_lib_v2.py:707] Step 19900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.28194886,
 'Loss/localization_loss': 0.012469847,
 'Loss/regularization_loss': 0.09414377,
 'Loss/total_loss': 0.3885625,
 'learning_rate': 0.054054994}
I0123 12:42:04.468038 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.28194886,
 'Loss/localization_loss': 0.012469847,
 'Loss/regularization_loss': 0.09414377,
 'Loss/total_loss': 0.3885625,
 'learning_rate': 0.054054994}
INFO:tensorflow:Step 20000 per-step time 0.641s
I0123 12:43:08.561310 139706229311360 model_lib_v2.py:707] Step 20000 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.3099317,
 'Loss/localization_loss': 0.01792999,
 'Loss/regularization_loss': 0.09412874,
 'Loss/total_loss': 0.42199045,
 'learning_rate': 0.0538146}
I0123 12:43:08.561619 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3099317,
 'Loss/localization_loss': 0.01792999,
 'Loss/regularization_loss': 0.09412874,
 'Loss/total_loss': 0.42199045,
 'learning_rate': 0.0538146}
INFO:tensorflow:Step 20100 per-step time 0.645s
I0123 12:44:13.040438 139706229311360 model_lib_v2.py:707] Step 20100 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.30708444,
 'Loss/localization_loss': 0.018148903,
 'Loss/regularization_loss': 0.09414736,
 'Loss/total_loss': 0.4193807,
 'learning_rate': 0.053573642}
I0123 12:44:13.040767 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30708444,
 'Loss/localization_loss': 0.018148903,
 'Loss/regularization_loss': 0.09414736,
 'Loss/total_loss': 0.4193807,
 'learning_rate': 0.053573642}
INFO:tensorflow:Step 20200 per-step time 0.641s
I0123 12:45:17.139473 139706229311360 model_lib_v2.py:707] Step 20200 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.26902854,
 'Loss/localization_loss': 0.029772656,
 'Loss/regularization_loss': 0.09416794,
 'Loss/total_loss': 0.39296916,
 'learning_rate': 0.053332124}
I0123 12:45:17.139769 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.26902854,
 'Loss/localization_loss': 0.029772656,
 'Loss/regularization_loss': 0.09416794,
 'Loss/total_loss': 0.39296916,
 'learning_rate': 0.053332124}
INFO:tensorflow:Step 20300 per-step time 0.639s
I0123 12:46:21.023640 139706229311360 model_lib_v2.py:707] Step 20300 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.25594407,
 'Loss/localization_loss': 0.01814051,
 'Loss/regularization_loss': 0.09415295,
 'Loss/total_loss': 0.36823753,
 'learning_rate': 0.053090062}
I0123 12:46:21.023944 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25594407,
 'Loss/localization_loss': 0.01814051,
 'Loss/regularization_loss': 0.09415295,
 'Loss/total_loss': 0.36823753,
 'learning_rate': 0.053090062}
INFO:tensorflow:Step 20400 per-step time 0.641s
I0123 12:47:25.165766 139706229311360 model_lib_v2.py:707] Step 20400 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.26555508,
 'Loss/localization_loss': 0.01717406,
 'Loss/regularization_loss': 0.09409663,
 'Loss/total_loss': 0.37682578,
 'learning_rate': 0.05284746}
I0123 12:47:25.166047 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.26555508,
 'Loss/localization_loss': 0.01717406,
 'Loss/regularization_loss': 0.09409663,
 'Loss/total_loss': 0.37682578,
 'learning_rate': 0.05284746}
INFO:tensorflow:Step 20500 per-step time 0.639s
I0123 12:48:29.095489 139706229311360 model_lib_v2.py:707] Step 20500 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.33731195,
 'Loss/localization_loss': 0.017706105,
 'Loss/regularization_loss': 0.09410284,
 'Loss/total_loss': 0.44912088,
 'learning_rate': 0.052604325}
I0123 12:48:29.095779 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33731195,
 'Loss/localization_loss': 0.017706105,
 'Loss/regularization_loss': 0.09410284,
 'Loss/total_loss': 0.44912088,
 'learning_rate': 0.052604325}
INFO:tensorflow:Step 20600 per-step time 0.642s
I0123 12:49:33.294761 139706229311360 model_lib_v2.py:707] Step 20600 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29128116,
 'Loss/localization_loss': 0.018047579,
 'Loss/regularization_loss': 0.09404613,
 'Loss/total_loss': 0.40337485,
 'learning_rate': 0.052360676}
I0123 12:49:33.295041 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29128116,
 'Loss/localization_loss': 0.018047579,
 'Loss/regularization_loss': 0.09404613,
 'Loss/total_loss': 0.40337485,
 'learning_rate': 0.052360676}
INFO:tensorflow:Step 20700 per-step time 0.645s
I0123 12:50:37.771266 139706229311360 model_lib_v2.py:707] Step 20700 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.27465734,
 'Loss/localization_loss': 0.043579392,
 'Loss/regularization_loss': 0.094031185,
 'Loss/total_loss': 0.41226792,
 'learning_rate': 0.05211652}
I0123 12:50:37.771540 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27465734,
 'Loss/localization_loss': 0.043579392,
 'Loss/regularization_loss': 0.094031185,
 'Loss/total_loss': 0.41226792,
 'learning_rate': 0.05211652}
INFO:tensorflow:Step 20800 per-step time 0.643s
I0123 12:51:42.085655 139706229311360 model_lib_v2.py:707] Step 20800 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.3206833,
 'Loss/localization_loss': 0.012931333,
 'Loss/regularization_loss': 0.09398094,
 'Loss/total_loss': 0.4275956,
 'learning_rate': 0.05187187}
I0123 12:51:42.085958 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3206833,
 'Loss/localization_loss': 0.012931333,
 'Loss/regularization_loss': 0.09398094,
 'Loss/total_loss': 0.4275956,
 'learning_rate': 0.05187187}
INFO:tensorflow:Step 20900 per-step time 0.642s
I0123 12:52:46.293357 139706229311360 model_lib_v2.py:707] Step 20900 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29483506,
 'Loss/localization_loss': 0.011464682,
 'Loss/regularization_loss': 0.09392528,
 'Loss/total_loss': 0.40022504,
 'learning_rate': 0.051626723}
I0123 12:52:46.293627 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29483506,
 'Loss/localization_loss': 0.011464682,
 'Loss/regularization_loss': 0.09392528,
 'Loss/total_loss': 0.40022504,
 'learning_rate': 0.051626723}
INFO:tensorflow:Step 21000 per-step time 0.640s
I0123 12:53:50.312822 139706229311360 model_lib_v2.py:707] Step 21000 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.33710402,
 'Loss/localization_loss': 0.031402607,
 'Loss/regularization_loss': 0.09388614,
 'Loss/total_loss': 0.46239278,
 'learning_rate': 0.0513811}
I0123 12:53:50.313095 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33710402,
 'Loss/localization_loss': 0.031402607,
 'Loss/regularization_loss': 0.09388614,
 'Loss/total_loss': 0.46239278,
 'learning_rate': 0.0513811}
INFO:tensorflow:Step 21100 per-step time 0.642s
I0123 12:54:54.564421 139706229311360 model_lib_v2.py:707] Step 21100 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29582432,
 'Loss/localization_loss': 0.017465543,
 'Loss/regularization_loss': 0.09386003,
 'Loss/total_loss': 0.40714988,
 'learning_rate': 0.051135015}
I0123 12:54:54.564747 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29582432,
 'Loss/localization_loss': 0.017465543,
 'Loss/regularization_loss': 0.09386003,
 'Loss/total_loss': 0.40714988,
 'learning_rate': 0.051135015}
INFO:tensorflow:Step 21200 per-step time 0.646s
I0123 12:55:59.129917 139706229311360 model_lib_v2.py:707] Step 21200 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.31103155,
 'Loss/localization_loss': 0.053711094,
 'Loss/regularization_loss': 0.09383644,
 'Loss/total_loss': 0.45857906,
 'learning_rate': 0.050888464}
I0123 12:55:59.130260 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31103155,
 'Loss/localization_loss': 0.053711094,
 'Loss/regularization_loss': 0.09383644,
 'Loss/total_loss': 0.45857906,
 'learning_rate': 0.050888464}
INFO:tensorflow:Step 21300 per-step time 0.645s
I0123 12:57:03.620060 139706229311360 model_lib_v2.py:707] Step 21300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.2628134,
 'Loss/localization_loss': 0.011737872,
 'Loss/regularization_loss': 0.093829274,
 'Loss/total_loss': 0.36838055,
 'learning_rate': 0.05064147}
I0123 12:57:03.620352 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2628134,
 'Loss/localization_loss': 0.011737872,
 'Loss/regularization_loss': 0.093829274,
 'Loss/total_loss': 0.36838055,
 'learning_rate': 0.05064147}
INFO:tensorflow:Step 21400 per-step time 0.644s
I0123 12:58:08.039330 139706229311360 model_lib_v2.py:707] Step 21400 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.39088708,
 'Loss/localization_loss': 0.00788187,
 'Loss/regularization_loss': 0.09382318,
 'Loss/total_loss': 0.49259216,
 'learning_rate': 0.05039404}
I0123 12:58:08.039595 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.39088708,
 'Loss/localization_loss': 0.00788187,
 'Loss/regularization_loss': 0.09382318,
 'Loss/total_loss': 0.49259216,
 'learning_rate': 0.05039404}
INFO:tensorflow:Step 21500 per-step time 0.643s
I0123 12:59:12.354693 139706229311360 model_lib_v2.py:707] Step 21500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.3578997,
 'Loss/localization_loss': 0.021961076,
 'Loss/regularization_loss': 0.093807176,
 'Loss/total_loss': 0.47366792,
 'learning_rate': 0.050146177}
I0123 12:59:12.354980 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3578997,
 'Loss/localization_loss': 0.021961076,
 'Loss/regularization_loss': 0.093807176,
 'Loss/total_loss': 0.47366792,
 'learning_rate': 0.050146177}
INFO:tensorflow:Step 21600 per-step time 0.641s
I0123 13:00:16.424781 139706229311360 model_lib_v2.py:707] Step 21600 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.4160522,
 'Loss/localization_loss': 0.025698824,
 'Loss/regularization_loss': 0.093816385,
 'Loss/total_loss': 0.5355674,
 'learning_rate': 0.04989791}
I0123 13:00:16.425070 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.4160522,
 'Loss/localization_loss': 0.025698824,
 'Loss/regularization_loss': 0.093816385,
 'Loss/total_loss': 0.5355674,
 'learning_rate': 0.04989791}
INFO:tensorflow:Step 21700 per-step time 0.643s
I0123 13:01:20.713320 139706229311360 model_lib_v2.py:707] Step 21700 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.25954783,
 'Loss/localization_loss': 0.0057919053,
 'Loss/regularization_loss': 0.093752325,
 'Loss/total_loss': 0.35909206,
 'learning_rate': 0.049649224}
I0123 13:01:20.713607 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25954783,
 'Loss/localization_loss': 0.0057919053,
 'Loss/regularization_loss': 0.093752325,
 'Loss/total_loss': 0.35909206,
 'learning_rate': 0.049649224}
INFO:tensorflow:Step 21800 per-step time 0.640s
I0123 13:02:24.758477 139706229311360 model_lib_v2.py:707] Step 21800 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.2827692,
 'Loss/localization_loss': 0.020943783,
 'Loss/regularization_loss': 0.09374389,
 'Loss/total_loss': 0.39745688,
 'learning_rate': 0.049400143}
I0123 13:02:24.758762 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2827692,
 'Loss/localization_loss': 0.020943783,
 'Loss/regularization_loss': 0.09374389,
 'Loss/total_loss': 0.39745688,
 'learning_rate': 0.049400143}
INFO:tensorflow:Step 21900 per-step time 0.641s
I0123 13:03:28.876546 139706229311360 model_lib_v2.py:707] Step 21900 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.21058424,
 'Loss/localization_loss': 0.014391282,
 'Loss/regularization_loss': 0.09373436,
 'Loss/total_loss': 0.31870988,
 'learning_rate': 0.049150676}
I0123 13:03:28.876828 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21058424,
 'Loss/localization_loss': 0.014391282,
 'Loss/regularization_loss': 0.09373436,
 'Loss/total_loss': 0.31870988,
 'learning_rate': 0.049150676}
INFO:tensorflow:Step 22000 per-step time 0.639s
I0123 13:04:32.799571 139706229311360 model_lib_v2.py:707] Step 22000 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.21170788,
 'Loss/localization_loss': 0.013087756,
 'Loss/regularization_loss': 0.09367338,
 'Loss/total_loss': 0.318469,
 'learning_rate': 0.048900835}
I0123 13:04:32.799846 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21170788,
 'Loss/localization_loss': 0.013087756,
 'Loss/regularization_loss': 0.09367338,
 'Loss/total_loss': 0.318469,
 'learning_rate': 0.048900835}
INFO:tensorflow:Step 22100 per-step time 0.647s
I0123 13:05:37.464426 139706229311360 model_lib_v2.py:707] Step 22100 per-step time 0.647s
INFO:tensorflow:{'Loss/classification_loss': 0.36087686,
 'Loss/localization_loss': 0.014638118,
 'Loss/regularization_loss': 0.093622334,
 'Loss/total_loss': 0.4691373,
 'learning_rate': 0.048650626}
I0123 13:05:37.464746 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.36087686,
 'Loss/localization_loss': 0.014638118,
 'Loss/regularization_loss': 0.093622334,
 'Loss/total_loss': 0.4691373,
 'learning_rate': 0.048650626}
INFO:tensorflow:Step 22200 per-step time 0.645s
I0123 13:06:41.950815 139706229311360 model_lib_v2.py:707] Step 22200 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.29098597,
 'Loss/localization_loss': 0.022130178,
 'Loss/regularization_loss': 0.09361759,
 'Loss/total_loss': 0.40673375,
 'learning_rate': 0.048400067}
I0123 13:06:41.951084 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29098597,
 'Loss/localization_loss': 0.022130178,
 'Loss/regularization_loss': 0.09361759,
 'Loss/total_loss': 0.40673375,
 'learning_rate': 0.048400067}
INFO:tensorflow:Step 22300 per-step time 0.644s
I0123 13:07:46.309553 139706229311360 model_lib_v2.py:707] Step 22300 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.4057059,
 'Loss/localization_loss': 0.012178864,
 'Loss/regularization_loss': 0.093603,
 'Loss/total_loss': 0.5114878,
 'learning_rate': 0.048149157}
I0123 13:07:46.309843 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.4057059,
 'Loss/localization_loss': 0.012178864,
 'Loss/regularization_loss': 0.093603,
 'Loss/total_loss': 0.5114878,
 'learning_rate': 0.048149157}
INFO:tensorflow:Step 22400 per-step time 0.639s
I0123 13:08:50.202278 139706229311360 model_lib_v2.py:707] Step 22400 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.32117268,
 'Loss/localization_loss': 0.013533677,
 'Loss/regularization_loss': 0.09357316,
 'Loss/total_loss': 0.42827952,
 'learning_rate': 0.04789791}
I0123 13:08:50.202545 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.32117268,
 'Loss/localization_loss': 0.013533677,
 'Loss/regularization_loss': 0.09357316,
 'Loss/total_loss': 0.42827952,
 'learning_rate': 0.04789791}
INFO:tensorflow:Step 22500 per-step time 0.643s
I0123 13:09:54.519349 139706229311360 model_lib_v2.py:707] Step 22500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.33383873,
 'Loss/localization_loss': 0.017355572,
 'Loss/regularization_loss': 0.09352026,
 'Loss/total_loss': 0.44471455,
 'learning_rate': 0.04764634}
I0123 13:09:54.519620 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33383873,
 'Loss/localization_loss': 0.017355572,
 'Loss/regularization_loss': 0.09352026,
 'Loss/total_loss': 0.44471455,
 'learning_rate': 0.04764634}
INFO:tensorflow:Step 22600 per-step time 0.646s
I0123 13:10:59.106294 139706229311360 model_lib_v2.py:707] Step 22600 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.27428612,
 'Loss/localization_loss': 0.015689908,
 'Loss/regularization_loss': 0.09347234,
 'Loss/total_loss': 0.38344836,
 'learning_rate': 0.047394462}
I0123 13:10:59.106573 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27428612,
 'Loss/localization_loss': 0.015689908,
 'Loss/regularization_loss': 0.09347234,
 'Loss/total_loss': 0.38344836,
 'learning_rate': 0.047394462}
INFO:tensorflow:Step 22700 per-step time 0.642s
I0123 13:12:03.260778 139706229311360 model_lib_v2.py:707] Step 22700 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.31313714,
 'Loss/localization_loss': 0.025391534,
 'Loss/regularization_loss': 0.09344882,
 'Loss/total_loss': 0.4319775,
 'learning_rate': 0.04714227}
I0123 13:12:03.261055 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31313714,
 'Loss/localization_loss': 0.025391534,
 'Loss/regularization_loss': 0.09344882,
 'Loss/total_loss': 0.4319775,
 'learning_rate': 0.04714227}
INFO:tensorflow:Step 22800 per-step time 0.644s
I0123 13:13:07.619523 139706229311360 model_lib_v2.py:707] Step 22800 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.30770117,
 'Loss/localization_loss': 0.008426239,
 'Loss/regularization_loss': 0.093440436,
 'Loss/total_loss': 0.40956786,
 'learning_rate': 0.046889797}
I0123 13:13:07.619822 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30770117,
 'Loss/localization_loss': 0.008426239,
 'Loss/regularization_loss': 0.093440436,
 'Loss/total_loss': 0.40956786,
 'learning_rate': 0.046889797}
INFO:tensorflow:Step 22900 per-step time 0.645s
I0123 13:14:12.153773 139706229311360 model_lib_v2.py:707] Step 22900 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.31052658,
 'Loss/localization_loss': 0.040365666,
 'Loss/regularization_loss': 0.09340423,
 'Loss/total_loss': 0.44429648,
 'learning_rate': 0.046637025}
I0123 13:14:12.154053 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31052658,
 'Loss/localization_loss': 0.040365666,
 'Loss/regularization_loss': 0.09340423,
 'Loss/total_loss': 0.44429648,
 'learning_rate': 0.046637025}
INFO:tensorflow:Step 23000 per-step time 0.643s
I0123 13:15:16.458415 139706229311360 model_lib_v2.py:707] Step 23000 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.24036367,
 'Loss/localization_loss': 0.024973867,
 'Loss/regularization_loss': 0.09338324,
 'Loss/total_loss': 0.35872078,
 'learning_rate': 0.046383996}
I0123 13:15:16.458698 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24036367,
 'Loss/localization_loss': 0.024973867,
 'Loss/regularization_loss': 0.09338324,
 'Loss/total_loss': 0.35872078,
 'learning_rate': 0.046383996}
INFO:tensorflow:Step 23100 per-step time 0.648s
I0123 13:16:21.227266 139706229311360 model_lib_v2.py:707] Step 23100 per-step time 0.648s
INFO:tensorflow:{'Loss/classification_loss': 0.30117485,
 'Loss/localization_loss': 0.022079531,
 'Loss/regularization_loss': 0.093348145,
 'Loss/total_loss': 0.41660252,
 'learning_rate': 0.04613069}
I0123 13:16:21.227533 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30117485,
 'Loss/localization_loss': 0.022079531,
 'Loss/regularization_loss': 0.093348145,
 'Loss/total_loss': 0.41660252,
 'learning_rate': 0.04613069}
INFO:tensorflow:Step 23200 per-step time 0.644s
I0123 13:17:25.631228 139706229311360 model_lib_v2.py:707] Step 23200 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.25450078,
 'Loss/localization_loss': 0.011690955,
 'Loss/regularization_loss': 0.09329549,
 'Loss/total_loss': 0.3594872,
 'learning_rate': 0.04587714}
I0123 13:17:25.631542 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25450078,
 'Loss/localization_loss': 0.011690955,
 'Loss/regularization_loss': 0.09329549,
 'Loss/total_loss': 0.3594872,
 'learning_rate': 0.04587714}
INFO:tensorflow:Step 23300 per-step time 0.641s
I0123 13:18:29.705097 139706229311360 model_lib_v2.py:707] Step 23300 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2776896,
 'Loss/localization_loss': 0.014632729,
 'Loss/regularization_loss': 0.09325454,
 'Loss/total_loss': 0.38557687,
 'learning_rate': 0.045623355}
I0123 13:18:29.705380 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2776896,
 'Loss/localization_loss': 0.014632729,
 'Loss/regularization_loss': 0.09325454,
 'Loss/total_loss': 0.38557687,
 'learning_rate': 0.045623355}
INFO:tensorflow:Step 23400 per-step time 0.646s
I0123 13:19:34.271421 139706229311360 model_lib_v2.py:707] Step 23400 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.24592155,
 'Loss/localization_loss': 0.013863071,
 'Loss/regularization_loss': 0.09324024,
 'Loss/total_loss': 0.35302484,
 'learning_rate': 0.045369323}
I0123 13:19:34.271768 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24592155,
 'Loss/localization_loss': 0.013863071,
 'Loss/regularization_loss': 0.09324024,
 'Loss/total_loss': 0.35302484,
 'learning_rate': 0.045369323}
INFO:tensorflow:Step 23500 per-step time 0.643s
I0123 13:20:38.538082 139706229311360 model_lib_v2.py:707] Step 23500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.22123255,
 'Loss/localization_loss': 0.010604003,
 'Loss/regularization_loss': 0.09323239,
 'Loss/total_loss': 0.32506895,
 'learning_rate': 0.045115083}
I0123 13:20:38.538361 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.22123255,
 'Loss/localization_loss': 0.010604003,
 'Loss/regularization_loss': 0.09323239,
 'Loss/total_loss': 0.32506895,
 'learning_rate': 0.045115083}
INFO:tensorflow:Step 23600 per-step time 0.645s
I0123 13:21:42.989419 139706229311360 model_lib_v2.py:707] Step 23600 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.2729299,
 'Loss/localization_loss': 0.015970767,
 'Loss/regularization_loss': 0.09321229,
 'Loss/total_loss': 0.38211298,
 'learning_rate': 0.044860635}
I0123 13:21:42.989691 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2729299,
 'Loss/localization_loss': 0.015970767,
 'Loss/regularization_loss': 0.09321229,
 'Loss/total_loss': 0.38211298,
 'learning_rate': 0.044860635}
INFO:tensorflow:Step 23700 per-step time 0.644s
I0123 13:22:47.412185 139706229311360 model_lib_v2.py:707] Step 23700 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.24451514,
 'Loss/localization_loss': 0.02105424,
 'Loss/regularization_loss': 0.09319431,
 'Loss/total_loss': 0.3587637,
 'learning_rate': 0.044605974}
I0123 13:22:47.412456 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24451514,
 'Loss/localization_loss': 0.02105424,
 'Loss/regularization_loss': 0.09319431,
 'Loss/total_loss': 0.3587637,
 'learning_rate': 0.044605974}
INFO:tensorflow:Step 23800 per-step time 0.641s
I0123 13:23:51.530093 139706229311360 model_lib_v2.py:707] Step 23800 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.25716016,
 'Loss/localization_loss': 0.012039635,
 'Loss/regularization_loss': 0.09315794,
 'Loss/total_loss': 0.36235774,
 'learning_rate': 0.044351134}
I0123 13:23:51.530377 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25716016,
 'Loss/localization_loss': 0.012039635,
 'Loss/regularization_loss': 0.09315794,
 'Loss/total_loss': 0.36235774,
 'learning_rate': 0.044351134}
INFO:tensorflow:Step 23900 per-step time 0.644s
I0123 13:24:55.941889 139706229311360 model_lib_v2.py:707] Step 23900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.32431552,
 'Loss/localization_loss': 0.029292773,
 'Loss/regularization_loss': 0.09310538,
 'Loss/total_loss': 0.44671366,
 'learning_rate': 0.044096105}
I0123 13:24:55.942199 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.32431552,
 'Loss/localization_loss': 0.029292773,
 'Loss/regularization_loss': 0.09310538,
 'Loss/total_loss': 0.44671366,
 'learning_rate': 0.044096105}
INFO:tensorflow:Step 24000 per-step time 0.645s
I0123 13:26:00.485518 139706229311360 model_lib_v2.py:707] Step 24000 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.29332164,
 'Loss/localization_loss': 0.011581618,
 'Loss/regularization_loss': 0.093092926,
 'Loss/total_loss': 0.3979962,
 'learning_rate': 0.043840922}
I0123 13:26:00.485812 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29332164,
 'Loss/localization_loss': 0.011581618,
 'Loss/regularization_loss': 0.093092926,
 'Loss/total_loss': 0.3979962,
 'learning_rate': 0.043840922}
INFO:tensorflow:Step 24100 per-step time 0.651s
I0123 13:27:05.567079 139706229311360 model_lib_v2.py:707] Step 24100 per-step time 0.651s
INFO:tensorflow:{'Loss/classification_loss': 0.2538514,
 'Loss/localization_loss': 0.014870389,
 'Loss/regularization_loss': 0.09308598,
 'Loss/total_loss': 0.36180776,
 'learning_rate': 0.043585572}
I0123 13:27:05.567408 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2538514,
 'Loss/localization_loss': 0.014870389,
 'Loss/regularization_loss': 0.09308598,
 'Loss/total_loss': 0.36180776,
 'learning_rate': 0.043585572}
INFO:tensorflow:Step 24200 per-step time 0.641s
I0123 13:28:09.635822 139706229311360 model_lib_v2.py:707] Step 24200 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2608899,
 'Loss/localization_loss': 0.016551472,
 'Loss/regularization_loss': 0.09304116,
 'Loss/total_loss': 0.3704825,
 'learning_rate': 0.043330073}
I0123 13:28:09.636123 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2608899,
 'Loss/localization_loss': 0.016551472,
 'Loss/regularization_loss': 0.09304116,
 'Loss/total_loss': 0.3704825,
 'learning_rate': 0.043330073}
INFO:tensorflow:Step 24300 per-step time 0.645s
I0123 13:29:14.163892 139706229311360 model_lib_v2.py:707] Step 24300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.30597606,
 'Loss/localization_loss': 0.018280154,
 'Loss/regularization_loss': 0.09300857,
 'Loss/total_loss': 0.4172648,
 'learning_rate': 0.04307444}
I0123 13:29:14.164170 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30597606,
 'Loss/localization_loss': 0.018280154,
 'Loss/regularization_loss': 0.09300857,
 'Loss/total_loss': 0.4172648,
 'learning_rate': 0.04307444}
INFO:tensorflow:Step 24400 per-step time 0.642s
I0123 13:30:18.377874 139706229311360 model_lib_v2.py:707] Step 24400 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.21212146,
 'Loss/localization_loss': 0.013448623,
 'Loss/regularization_loss': 0.09298284,
 'Loss/total_loss': 0.3185529,
 'learning_rate': 0.04281868}
I0123 13:30:18.378145 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21212146,
 'Loss/localization_loss': 0.013448623,
 'Loss/regularization_loss': 0.09298284,
 'Loss/total_loss': 0.3185529,
 'learning_rate': 0.04281868}
INFO:tensorflow:Step 24500 per-step time 0.641s
I0123 13:31:22.484993 139706229311360 model_lib_v2.py:707] Step 24500 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2651904,
 'Loss/localization_loss': 0.013821077,
 'Loss/regularization_loss': 0.0929415,
 'Loss/total_loss': 0.37195295,
 'learning_rate': 0.042562805}
I0123 13:31:22.485275 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2651904,
 'Loss/localization_loss': 0.013821077,
 'Loss/regularization_loss': 0.0929415,
 'Loss/total_loss': 0.37195295,
 'learning_rate': 0.042562805}
INFO:tensorflow:Step 24600 per-step time 0.643s
I0123 13:32:26.764012 139706229311360 model_lib_v2.py:707] Step 24600 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.2554271,
 'Loss/localization_loss': 0.017306734,
 'Loss/regularization_loss': 0.09291727,
 'Loss/total_loss': 0.3656511,
 'learning_rate': 0.04230683}
I0123 13:32:26.764289 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2554271,
 'Loss/localization_loss': 0.017306734,
 'Loss/regularization_loss': 0.09291727,
 'Loss/total_loss': 0.3656511,
 'learning_rate': 0.04230683}
INFO:tensorflow:Step 24700 per-step time 0.642s
I0123 13:33:30.963247 139706229311360 model_lib_v2.py:707] Step 24700 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.38368958,
 'Loss/localization_loss': 0.015524857,
 'Loss/regularization_loss': 0.09286607,
 'Loss/total_loss': 0.4920805,
 'learning_rate': 0.042050745}
I0123 13:33:30.963555 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.38368958,
 'Loss/localization_loss': 0.015524857,
 'Loss/regularization_loss': 0.09286607,
 'Loss/total_loss': 0.4920805,
 'learning_rate': 0.042050745}
INFO:tensorflow:Step 24800 per-step time 0.644s
I0123 13:34:35.318193 139706229311360 model_lib_v2.py:707] Step 24800 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.3103263,
 'Loss/localization_loss': 0.010406563,
 'Loss/regularization_loss': 0.09281407,
 'Loss/total_loss': 0.41354692,
 'learning_rate': 0.04179459}
I0123 13:34:35.318495 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3103263,
 'Loss/localization_loss': 0.010406563,
 'Loss/regularization_loss': 0.09281407,
 'Loss/total_loss': 0.41354692,
 'learning_rate': 0.04179459}
INFO:tensorflow:Step 24900 per-step time 0.644s
I0123 13:35:39.735392 139706229311360 model_lib_v2.py:707] Step 24900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.24705991,
 'Loss/localization_loss': 0.018297436,
 'Loss/regularization_loss': 0.09276485,
 'Loss/total_loss': 0.3581222,
 'learning_rate': 0.04153835}
I0123 13:35:39.735681 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24705991,
 'Loss/localization_loss': 0.018297436,
 'Loss/regularization_loss': 0.09276485,
 'Loss/total_loss': 0.3581222,
 'learning_rate': 0.04153835}
INFO:tensorflow:Step 25000 per-step time 0.638s
I0123 13:36:43.587889 139706229311360 model_lib_v2.py:707] Step 25000 per-step time 0.638s
INFO:tensorflow:{'Loss/classification_loss': 0.24062178,
 'Loss/localization_loss': 0.016733974,
 'Loss/regularization_loss': 0.09272963,
 'Loss/total_loss': 0.35008538,
 'learning_rate': 0.04128206}
I0123 13:36:43.588164 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24062178,
 'Loss/localization_loss': 0.016733974,
 'Loss/regularization_loss': 0.09272963,
 'Loss/total_loss': 0.35008538,
 'learning_rate': 0.04128206}
INFO:tensorflow:Step 25100 per-step time 0.647s
I0123 13:37:48.273289 139706229311360 model_lib_v2.py:707] Step 25100 per-step time 0.647s
INFO:tensorflow:{'Loss/classification_loss': 0.21411954,
 'Loss/localization_loss': 0.020920731,
 'Loss/regularization_loss': 0.09270904,
 'Loss/total_loss': 0.3277493,
 'learning_rate': 0.041025713}
I0123 13:37:48.273579 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21411954,
 'Loss/localization_loss': 0.020920731,
 'Loss/regularization_loss': 0.09270904,
 'Loss/total_loss': 0.3277493,
 'learning_rate': 0.041025713}
Traceback (most recent call last):
  File "model_main_tf2.py", line 115, in <module>
    tf.compat.v1.app.run()
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 303, in run
    _run_main(main, args)
  File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "model_main_tf2.py", line 112, in main
    record_summaries=FLAGS.record_summaries)
  File "/usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py", line 685, in train_loop
    losses_dict = _dist_train_step(train_input_iter)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py", line 150, in error_handler
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 910, in __call__
    result = self._call(*args, **kwds)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 942, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 3131, in __call__
    filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 1960, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 603, in call
    ctx=ctx)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    inputs, attrs, num_outputs)
KeyboardInterrupt


!python model_main_tf2.py --model_dir=models/my_ssd_mobilenet --pipeline_config_path=models/my_ssd_mobilenet/pipeline.config --checkpoint_dir=models/my_ssd_mobilenet

Traceback (most recent call last):
  File "model_main_tf2.py", line 32, in <module>
    from object_detection import model_lib_v2
ModuleNotFoundError: No module named 'object_detection'


!pwd

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd


!python ./exporter_main_v2.py --input_type image_tensor --pipeline_config_path ./models/my_ssd_mobilenet/pipeline.config --trained_checkpoint_dir ./models/my_ssd_mobilenet/ --output_directory ./exported-models/my_model

2022-01-23 13:47:36.848232: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
W0123 13:47:37.120375 139807483082624 deprecation.py:619] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f26c0481790>, because it is not built.
W0123 13:48:40.042584 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f26c0481790>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c0332cd0>, because it is not built.
W0123 13:48:40.180683 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c0332cd0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa7c4d0>, because it is not built.
W0123 13:48:40.180946 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa7c4d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263abc3fd0>, because it is not built.
W0123 13:48:40.181043 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263abc3fd0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ae03c90>, because it is not built.
W0123 13:48:40.181126 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ae03c90>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0790>, because it is not built.
W0123 13:48:40.181198 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0790>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaa0d10>, because it is not built.
W0123 13:48:40.181265 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaa0d10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c00ddc10>, because it is not built.
W0123 13:48:40.181330 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c00ddc10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa03d0>, because it is not built.
W0123 13:48:40.181404 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa03d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac446d0>, because it is not built.
W0123 13:48:40.181459 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac446d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ac44f10>, because it is not built.
W0123 13:48:40.181513 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ac44f10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26521cef90>, because it is not built.
W0123 13:48:40.181567 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26521cef90>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac74990>, because it is not built.
W0123 13:48:40.181622 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac74990>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339a90>, because it is not built.
W0123 13:48:40.181675 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339a90>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21150>, because it is not built.
W0123 13:48:40.181739 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21150>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa219d0>, because it is not built.
W0123 13:48:40.181796 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa219d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21f10>, because it is not built.
W0123 13:48:40.181851 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21f10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab22890>, because it is not built.
W0123 13:48:40.181906 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab22890>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1b410>, because it is not built.
W0123 13:48:40.181965 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1b410>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa1b390>, because it is not built.
W0123 13:48:40.182022 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa1b390>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1bc10>, because it is not built.
W0123 13:48:40.182077 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1bc10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339ad0>, because it is not built.
W0123 13:48:40.182204 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339ad0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa15ed0>, because it is not built.
W0123 13:48:40.182262 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa15ed0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5dcd0>, because it is not built.
W0123 13:48:40.182318 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5dcd0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2b790>, because it is not built.
W0123 13:48:40.182373 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2b790>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab47790>, because it is not built.
W0123 13:48:40.182427 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab47790>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2bb90>, because it is not built.
W0123 13:48:40.182485 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2bb90>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa2bc50>, because it is not built.
W0123 13:48:40.182540 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa2bc50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaad1d0>, because it is not built.
W0123 13:48:40.182594 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaad1d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab1aa10>, because it is not built.
W0123 13:48:40.182648 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab1aa10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ae64b50>, because it is not built.
W0123 13:48:40.182706 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ae64b50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a97bcd0>, because it is not built.
W0123 13:48:40.182767 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a97bcd0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ab5fad0>, because it is not built.
W0123 13:48:40.182822 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ab5fad0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5d110>, because it is not built.
W0123 13:48:40.182875 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5d110>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6610>, because it is not built.
W0123 13:48:40.182928 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6610>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac44250>, because it is not built.
W0123 13:48:40.182985 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac44250>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6850>, because it is not built.
W0123 13:48:40.183042 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6850>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339b10>, because it is not built.
W0123 13:48:40.183103 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339b10>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a99f6d0>, because it is not built.
W0123 13:48:40.183159 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a99f6d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0490>, because it is not built.
W0123 13:48:40.183212 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0490>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9400d0>, because it is not built.
W0123 13:48:40.183266 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9400d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a940a50>, because it is not built.
W0123 13:48:40.183320 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a940a50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940c50>, because it is not built.
W0123 13:48:40.183383 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940c50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ae222d0>, because it is not built.
W0123 13:48:40.183437 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ae222d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940e10>, because it is not built.
W0123 13:48:40.232452 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940e10>, because it is not built.
2022-01-23 13:48:49.249368: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
W0123 13:49:27.565280 139807483082624 save.py:268] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 260). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: ./exported-models/my_model/saved_model/assets
I0123 13:49:36.735169 139807483082624 builder_impl.py:784] Assets written to: ./exported-models/my_model/saved_model/assets
INFO:tensorflow:Writing pipeline config file to ./exported-models/my_model/pipeline.config
I0123 13:49:37.740982 139807483082624 config_util.py:254] Writing pipeline config file to ./exported-models/my_model/pipeline.config


!python model_main_tf2.py --model_dir=models/my_rcnn_imagenet --pipeline_config_path=models/my_rcnn_imagenet/pipeline.config

2022-01-23 13:57:12.252312: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0123 13:57:12.255908 140287364614016 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I0123 13:57:13.178883 140287364614016 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0123 13:57:13.179064 140287364614016 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0123 13:57:13.214003 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['./annotations/train.record']
I0123 13:57:13.264912 140287364614016 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/train.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/train.record']
I0123 13:57:13.265283 140287364614016 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/train.record']
INFO:tensorflow:Number of filenames to read: 1
I0123 13:57:13.265372 140287364614016 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0123 13:57:13.265424 140287364614016 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0123 13:57:13.268596 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0123 13:57:13.289052 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0123 13:57:20.676505 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0123 13:57:25.244715 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2022-01-23 13:57:29.438655: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at multi_device_iterator_ops.cc:789 : NOT_FOUND: Resource AnonymousMultiDeviceIterator/AnonymousMultiDeviceIterator0/N10tensorflow4data12_GLOBAL__N_119MultiDeviceIteratorE does not exist.
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:depth of additional conv before box predictor: 0
I0123 13:57:39.527516 140281796044544 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0123 13:57:46.521125 140281796044544 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See `tf.nn.softmax_cross_entropy_with_logits_v2`.

W0123 13:57:51.869955 140281796044544 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See `tf.nn.softmax_cross_entropy_with_logits_v2`.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0123 13:58:29.798123 140281815717632 deprecation.py:551] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
INFO:tensorflow:Step 62100 per-step time 1.036s
I0123 14:00:12.977305 140287364614016 model_lib_v2.py:707] Step 62100 per-step time 1.036s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000492997,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 90.7686,
 'Loss/RPNLoss/objectness_loss': 1.3280555,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 92.09715,
 'learning_rate': 0.052598022}
I0123 14:00:12.977638 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000492997,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 90.7686,
 'Loss/RPNLoss/objectness_loss': 1.3280555,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 92.09715,
 'learning_rate': 0.052598022}
INFO:tensorflow:Step 62200 per-step time 0.374s
I0123 14:00:50.394164 140287364614016 model_lib_v2.py:707] Step 62200 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0026617302,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1758826,
 'Loss/RPNLoss/objectness_loss': 0.15363389,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.3321782,
 'learning_rate': 0.05235598}
I0123 14:00:50.394497 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0026617302,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1758826,
 'Loss/RPNLoss/objectness_loss': 0.15363389,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.3321782,
 'learning_rate': 0.05235598}
INFO:tensorflow:Step 62300 per-step time 0.375s
I0123 14:01:27.904536 140287364614016 model_lib_v2.py:707] Step 62300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00060942845,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.3877,
 'Loss/RPNLoss/objectness_loss': 1.1692628,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 70.55757,
 'learning_rate': 0.05211423}
I0123 14:01:27.904881 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00060942845,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.3877,
 'Loss/RPNLoss/objectness_loss': 1.1692628,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 70.55757,
 'learning_rate': 0.05211423}
INFO:tensorflow:Step 62400 per-step time 0.378s
I0123 14:02:05.710331 140287364614016 model_lib_v2.py:707] Step 62400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.021919131,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 91.50695,
 'Loss/RPNLoss/objectness_loss': 3.7153988,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.24427,
 'learning_rate': 0.051872768}
I0123 14:02:05.710622 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.021919131,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 91.50695,
 'Loss/RPNLoss/objectness_loss': 3.7153988,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.24427,
 'learning_rate': 0.051872768}
INFO:tensorflow:Step 62500 per-step time 0.377s
I0123 14:02:43.399406 140287364614016 model_lib_v2.py:707] Step 62500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0004768371,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.623506,
 'Loss/RPNLoss/objectness_loss': 3.5612764,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.185259,
 'learning_rate': 0.051631603}
I0123 14:02:43.399716 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0004768371,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.623506,
 'Loss/RPNLoss/objectness_loss': 3.5612764,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.185259,
 'learning_rate': 0.051631603}
INFO:tensorflow:Step 62600 per-step time 0.371s
I0123 14:03:20.505567 140287364614016 model_lib_v2.py:707] Step 62600 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014855636,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 44.965633,
 'Loss/RPNLoss/objectness_loss': 1.3126142,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 46.27973,
 'learning_rate': 0.051390734}
I0123 14:03:20.505922 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014855636,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 44.965633,
 'Loss/RPNLoss/objectness_loss': 1.3126142,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 46.27973,
 'learning_rate': 0.051390734}
INFO:tensorflow:Step 62700 per-step time 0.373s
I0123 14:03:57.809883 140287364614016 model_lib_v2.py:707] Step 62700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018344786,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.794763,
 'Loss/RPNLoss/objectness_loss': 0.51534635,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.328454,
 'learning_rate': 0.05115015}
I0123 14:03:57.810185 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018344786,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.794763,
 'Loss/RPNLoss/objectness_loss': 0.51534635,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.328454,
 'learning_rate': 0.05115015}
INFO:tensorflow:Step 62800 per-step time 0.374s
I0123 14:04:35.192644 140287364614016 model_lib_v2.py:707] Step 62800 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00088795327,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 284.0681,
 'Loss/RPNLoss/objectness_loss': 0.2403039,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 284.3093,
 'learning_rate': 0.05090988}
I0123 14:04:35.192946 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00088795327,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 284.0681,
 'Loss/RPNLoss/objectness_loss': 0.2403039,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 284.3093,
 'learning_rate': 0.05090988}
INFO:tensorflow:Step 62900 per-step time 0.376s
I0123 14:05:12.833030 140287364614016 model_lib_v2.py:707] Step 62900 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005971049,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.553404,
 'Loss/RPNLoss/objectness_loss': 1.6263932,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.1803942,
 'learning_rate': 0.05066991}
I0123 14:05:12.833358 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005971049,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.553404,
 'Loss/RPNLoss/objectness_loss': 1.6263932,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.1803942,
 'learning_rate': 0.05066991}
INFO:tensorflow:Step 63000 per-step time 0.373s
I0123 14:05:50.118892 140287364614016 model_lib_v2.py:707] Step 63000 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0021740608,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.52805334,
 'Loss/RPNLoss/objectness_loss': 4.8757257,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.405953,
 'learning_rate': 0.05043024}
I0123 14:05:50.119196 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0021740608,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.52805334,
 'Loss/RPNLoss/objectness_loss': 4.8757257,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.405953,
 'learning_rate': 0.05043024}
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.372031 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.373293 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.374841 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.375624 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.377091 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.377877 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.379298 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.380093 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.381747 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.382510 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Step 63100 per-step time 0.402s
I0123 14:06:30.362667 140287364614016 model_lib_v2.py:707] Step 63100 per-step time 0.402s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0018812593,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 132.10474,
 'Loss/RPNLoss/objectness_loss': 0.6295264,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 132.73615,
 'learning_rate': 0.050190877}
I0123 14:06:30.362978 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0018812593,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 132.10474,
 'Loss/RPNLoss/objectness_loss': 0.6295264,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 132.73615,
 'learning_rate': 0.050190877}
INFO:tensorflow:Step 63200 per-step time 0.373s
I0123 14:07:07.644792 140287364614016 model_lib_v2.py:707] Step 63200 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014057412,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 133.76497,
 'Loss/RPNLoss/objectness_loss': 0.65616226,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 134.42253,
 'learning_rate': 0.04995183}
I0123 14:07:07.645141 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014057412,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 133.76497,
 'Loss/RPNLoss/objectness_loss': 0.65616226,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 134.42253,
 'learning_rate': 0.04995183}
INFO:tensorflow:Step 63300 per-step time 0.371s
I0123 14:07:44.710620 140287364614016 model_lib_v2.py:707] Step 63300 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0008546056,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 274.3549,
 'Loss/RPNLoss/objectness_loss': 5.545502,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 279.90125,
 'learning_rate': 0.04971307}
I0123 14:07:44.710935 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0008546056,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 274.3549,
 'Loss/RPNLoss/objectness_loss': 5.545502,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 279.90125,
 'learning_rate': 0.04971307}
INFO:tensorflow:Step 63400 per-step time 0.375s
I0123 14:08:22.212358 140287364614016 model_lib_v2.py:707] Step 63400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0025572977,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 188.43549,
 'Loss/RPNLoss/objectness_loss': 17.72029,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 206.15833,
 'learning_rate': 0.04947465}
I0123 14:08:22.212655 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0025572977,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 188.43549,
 'Loss/RPNLoss/objectness_loss': 17.72029,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 206.15833,
 'learning_rate': 0.04947465}
INFO:tensorflow:Step 63500 per-step time 0.378s
I0123 14:09:00.006186 140287364614016 model_lib_v2.py:707] Step 63500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005873506,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 155.0148,
 'Loss/RPNLoss/objectness_loss': 1.0824015,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 156.0978,
 'learning_rate': 0.04923654}
I0123 14:09:00.006499 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005873506,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 155.0148,
 'Loss/RPNLoss/objectness_loss': 1.0824015,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 156.0978,
 'learning_rate': 0.04923654}
INFO:tensorflow:Step 63600 per-step time 0.375s
I0123 14:09:37.546933 140287364614016 model_lib_v2.py:707] Step 63600 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0011416264,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 338.46606,
 'Loss/RPNLoss/objectness_loss': 2.061144,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 340.52835,
 'learning_rate': 0.048998754}
I0123 14:09:37.547255 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0011416264,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 338.46606,
 'Loss/RPNLoss/objectness_loss': 2.061144,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 340.52835,
 'learning_rate': 0.048998754}
INFO:tensorflow:Step 63700 per-step time 0.371s
I0123 14:10:14.661952 140287364614016 model_lib_v2.py:707] Step 63700 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0016434728,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 29.517916,
 'Loss/RPNLoss/objectness_loss': 8.221753,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 37.74131,
 'learning_rate': 0.048761286}
I0123 14:10:14.662243 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0016434728,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 29.517916,
 'Loss/RPNLoss/objectness_loss': 8.221753,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 37.74131,
 'learning_rate': 0.048761286}
INFO:tensorflow:Step 63800 per-step time 0.372s
I0123 14:10:51.910885 140287364614016 model_lib_v2.py:707] Step 63800 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007961419,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 35.16668,
 'Loss/RPNLoss/objectness_loss': 4.527713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 39.69519,
 'learning_rate': 0.04852413}
I0123 14:10:51.911220 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007961419,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 35.16668,
 'Loss/RPNLoss/objectness_loss': 4.527713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 39.69519,
 'learning_rate': 0.04852413}
INFO:tensorflow:Step 63900 per-step time 0.370s
I0123 14:11:28.960614 140287364614016 model_lib_v2.py:707] Step 63900 per-step time 0.370s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00039884815,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.4438143,
 'Loss/RPNLoss/objectness_loss': 8.473839,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.918052,
 'learning_rate': 0.048287313}
I0123 14:11:28.960958 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00039884815,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.4438143,
 'Loss/RPNLoss/objectness_loss': 8.473839,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.918052,
 'learning_rate': 0.048287313}
INFO:tensorflow:Step 64000 per-step time 0.373s
I0123 14:12:06.213313 140287364614016 model_lib_v2.py:707] Step 64000 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00033277879,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.043744,
 'Loss/RPNLoss/objectness_loss': 7.984674,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 14.028751,
 'learning_rate': 0.04805083}
I0123 14:12:06.213598 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00033277879,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.043744,
 'Loss/RPNLoss/objectness_loss': 7.984674,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 14.028751,
 'learning_rate': 0.04805083}
INFO:tensorflow:Step 64100 per-step time 0.413s
I0123 14:12:47.546821 140287364614016 model_lib_v2.py:707] Step 64100 per-step time 0.413s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.7242764,
 'Loss/BoxClassifierLoss/localization_loss': 0.61703694,
 'Loss/RPNLoss/localization_loss': 329.75446,
 'Loss/RPNLoss/objectness_loss': 1.200119,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 332.2959,
 'learning_rate': 0.047814675}
I0123 14:12:47.547163 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.7242764,
 'Loss/BoxClassifierLoss/localization_loss': 0.61703694,
 'Loss/RPNLoss/localization_loss': 329.75446,
 'Loss/RPNLoss/objectness_loss': 1.200119,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 332.2959,
 'learning_rate': 0.047814675}
INFO:tensorflow:Step 64200 per-step time 0.374s
I0123 14:13:24.963876 140287364614016 model_lib_v2.py:707] Step 64200 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00055178674,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 319.90347,
 'Loss/RPNLoss/objectness_loss': 2.7614956,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 322.66553,
 'learning_rate': 0.047578853}
I0123 14:13:24.964197 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00055178674,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 319.90347,
 'Loss/RPNLoss/objectness_loss': 2.7614956,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 322.66553,
 'learning_rate': 0.047578853}
INFO:tensorflow:Step 64300 per-step time 0.376s
I0123 14:14:02.543442 140287364614016 model_lib_v2.py:707] Step 64300 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.009720907,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 97.20221,
 'Loss/RPNLoss/objectness_loss': 1.3665836,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 98.578514,
 'learning_rate': 0.04734336}
I0123 14:14:02.543759 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.009720907,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 97.20221,
 'Loss/RPNLoss/objectness_loss': 1.3665836,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 98.578514,
 'learning_rate': 0.04734336}
INFO:tensorflow:Step 64400 per-step time 0.376s
I0123 14:14:40.139990 140287364614016 model_lib_v2.py:707] Step 64400 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0004939477,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.6672,
 'Loss/RPNLoss/objectness_loss': 6.200095,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 59.867786,
 'learning_rate': 0.04710822}
I0123 14:14:40.140307 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0004939477,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.6672,
 'Loss/RPNLoss/objectness_loss': 6.200095,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 59.867786,
 'learning_rate': 0.04710822}
INFO:tensorflow:Step 64500 per-step time 0.374s
I0123 14:15:17.541444 140287364614016 model_lib_v2.py:707] Step 64500 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016984738,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.09159162,
 'Loss/RPNLoss/objectness_loss': 2.2853708,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.3939471,
 'learning_rate': 0.046873417}
I0123 14:15:17.541775 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016984738,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.09159162,
 'Loss/RPNLoss/objectness_loss': 2.2853708,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.3939471,
 'learning_rate': 0.046873417}
INFO:tensorflow:Step 64600 per-step time 0.374s
I0123 14:15:54.930982 140287364614016 model_lib_v2.py:707] Step 64600 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.004966923,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 197.84238,
 'Loss/RPNLoss/objectness_loss': 2.8513966,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 200.69875,
 'learning_rate': 0.046638947}
I0123 14:15:54.931288 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.004966923,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 197.84238,
 'Loss/RPNLoss/objectness_loss': 2.8513966,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 200.69875,
 'learning_rate': 0.046638947}
INFO:tensorflow:Step 64700 per-step time 0.373s
I0123 14:16:32.224833 140287364614016 model_lib_v2.py:707] Step 64700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018832427,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 49.595833,
 'Loss/RPNLoss/objectness_loss': 2.1529121,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 51.76758,
 'learning_rate': 0.046404842}
I0123 14:16:32.225135 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018832427,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 49.595833,
 'Loss/RPNLoss/objectness_loss': 2.1529121,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 51.76758,
 'learning_rate': 0.046404842}
INFO:tensorflow:Step 64800 per-step time 0.381s
I0123 14:17:10.304227 140287364614016 model_lib_v2.py:707] Step 64800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.087695256,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 350.02173,
 'Loss/RPNLoss/objectness_loss': 1.1198058,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 351.22922,
 'learning_rate': 0.046171073}
I0123 14:17:10.304567 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.087695256,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 350.02173,
 'Loss/RPNLoss/objectness_loss': 1.1198058,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 351.22922,
 'learning_rate': 0.046171073}
INFO:tensorflow:Step 64900 per-step time 0.374s
I0123 14:17:47.677740 140287364614016 model_lib_v2.py:707] Step 64900 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014196797,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 534.66064,
 'Loss/RPNLoss/objectness_loss': 6.2795687,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 540.94165,
 'learning_rate': 0.04593765}
I0123 14:17:47.678031 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014196797,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 534.66064,
 'Loss/RPNLoss/objectness_loss': 6.2795687,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 540.94165,
 'learning_rate': 0.04593765}
INFO:tensorflow:Step 65000 per-step time 0.372s
I0123 14:18:24.830154 140287364614016 model_lib_v2.py:707] Step 65000 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.015616467,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 68.459785,
 'Loss/RPNLoss/objectness_loss': 4.759358,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.23476,
 'learning_rate': 0.045704603}
I0123 14:18:24.830453 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.015616467,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 68.459785,
 'Loss/RPNLoss/objectness_loss': 4.759358,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.23476,
 'learning_rate': 0.045704603}
INFO:tensorflow:Step 65100 per-step time 0.407s
I0123 14:19:05.559003 140287364614016 model_lib_v2.py:707] Step 65100 per-step time 0.407s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.031776946,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 160.14133,
 'Loss/RPNLoss/objectness_loss': 0.8498359,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 161.02293,
 'learning_rate': 0.045471888}
I0123 14:19:05.559355 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.031776946,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 160.14133,
 'Loss/RPNLoss/objectness_loss': 0.8498359,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 161.02293,
 'learning_rate': 0.045471888}
INFO:tensorflow:Step 65200 per-step time 0.377s
I0123 14:19:43.209518 140287364614016 model_lib_v2.py:707] Step 65200 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016326264,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 52.032177,
 'Loss/RPNLoss/objectness_loss': 0.745766,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 52.79427,
 'learning_rate': 0.045239534}
I0123 14:19:43.209821 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016326264,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 52.032177,
 'Loss/RPNLoss/objectness_loss': 0.745766,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 52.79427,
 'learning_rate': 0.045239534}
INFO:tensorflow:Step 65300 per-step time 0.374s
I0123 14:20:20.585823 140287364614016 model_lib_v2.py:707] Step 65300 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007256042,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 55.534836,
 'Loss/RPNLoss/objectness_loss': 0.20595771,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 55.74152,
 'learning_rate': 0.04500754}
I0123 14:20:20.586178 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007256042,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 55.534836,
 'Loss/RPNLoss/objectness_loss': 0.20595771,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 55.74152,
 'learning_rate': 0.04500754}
INFO:tensorflow:Step 65400 per-step time 0.377s
I0123 14:20:58.322028 140287364614016 model_lib_v2.py:707] Step 65400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.043169294,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1413682,
 'Loss/RPNLoss/objectness_loss': 7.871082,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 9.055619,
 'learning_rate': 0.044775933}
I0123 14:20:58.322373 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.043169294,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1413682,
 'Loss/RPNLoss/objectness_loss': 7.871082,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 9.055619,
 'learning_rate': 0.044775933}
INFO:tensorflow:Step 65500 per-step time 0.376s
I0123 14:21:35.873519 140287364614016 model_lib_v2.py:707] Step 65500 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0011358516,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 197.06683,
 'Loss/RPNLoss/objectness_loss': 12.7355385,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 209.80351,
 'learning_rate': 0.044544667}
I0123 14:21:35.873832 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0011358516,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 197.06683,
 'Loss/RPNLoss/objectness_loss': 12.7355385,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 209.80351,
 'learning_rate': 0.044544667}
INFO:tensorflow:Step 65600 per-step time 0.370s
I0123 14:22:12.915478 140287364614016 model_lib_v2.py:707] Step 65600 per-step time 0.370s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00078448025,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 309.0344,
 'Loss/RPNLoss/objectness_loss': 1.9329586,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 310.96814,
 'learning_rate': 0.044313792}
I0123 14:22:12.915791 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00078448025,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 309.0344,
 'Loss/RPNLoss/objectness_loss': 1.9329586,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 310.96814,
 'learning_rate': 0.044313792}
INFO:tensorflow:Step 65700 per-step time 0.378s
I0123 14:22:50.677616 140287364614016 model_lib_v2.py:707] Step 65700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 2.0768228,
 'Loss/BoxClassifierLoss/localization_loss': 1.6045876,
 'Loss/RPNLoss/localization_loss': 2.2951121,
 'Loss/RPNLoss/objectness_loss': 0.1115912,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.088114,
 'learning_rate': 0.044083264}
I0123 14:22:50.677938 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 2.0768228,
 'Loss/BoxClassifierLoss/localization_loss': 1.6045876,
 'Loss/RPNLoss/localization_loss': 2.2951121,
 'Loss/RPNLoss/objectness_loss': 0.1115912,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.088114,
 'learning_rate': 0.044083264}
INFO:tensorflow:Step 65800 per-step time 0.376s
I0123 14:23:28.280859 140287364614016 model_lib_v2.py:707] Step 65800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00073656684,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.14475,
 'Loss/RPNLoss/objectness_loss': 1.62201,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 67.7675,
 'learning_rate': 0.043853115}
I0123 14:23:28.281158 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00073656684,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.14475,
 'Loss/RPNLoss/objectness_loss': 1.62201,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 67.7675,
 'learning_rate': 0.043853115}
INFO:tensorflow:Step 65900 per-step time 0.375s
I0123 14:24:05.789642 140287364614016 model_lib_v2.py:707] Step 65900 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0053697806,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.401436,
 'Loss/RPNLoss/objectness_loss': 2.7582493,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 72.165054,
 'learning_rate': 0.043623343}
I0123 14:24:05.789956 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0053697806,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.401436,
 'Loss/RPNLoss/objectness_loss': 2.7582493,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 72.165054,
 'learning_rate': 0.043623343}
INFO:tensorflow:Step 66000 per-step time 0.379s
I0123 14:24:43.694464 140287364614016 model_lib_v2.py:707] Step 66000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000523897,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 59.15332,
 'Loss/RPNLoss/objectness_loss': 4.1302223,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 63.284065,
 'learning_rate': 0.043393955}
I0123 14:24:43.694800 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000523897,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 59.15332,
 'Loss/RPNLoss/objectness_loss': 4.1302223,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 63.284065,
 'learning_rate': 0.043393955}
INFO:tensorflow:Step 66100 per-step time 0.398s
I0123 14:25:23.534175 140287364614016 model_lib_v2.py:707] Step 66100 per-step time 0.398s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005418784,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.4355414,
 'Loss/RPNLoss/objectness_loss': 2.4417148,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.877798,
 'learning_rate': 0.04316494}
I0123 14:25:23.534489 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005418784,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.4355414,
 'Loss/RPNLoss/objectness_loss': 2.4417148,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.877798,
 'learning_rate': 0.04316494}
INFO:tensorflow:Step 66200 per-step time 0.375s
I0123 14:26:01.000761 140287364614016 model_lib_v2.py:707] Step 66200 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005302159,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 234.18225,
 'Loss/RPNLoss/objectness_loss': 4.2425914,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 238.42537,
 'learning_rate': 0.042936318}
I0123 14:26:01.001060 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005302159,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 234.18225,
 'Loss/RPNLoss/objectness_loss': 4.2425914,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 238.42537,
 'learning_rate': 0.042936318}
INFO:tensorflow:Step 66300 per-step time 0.377s
I0123 14:26:38.728879 140287364614016 model_lib_v2.py:707] Step 66300 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007565207,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.5628887,
 'Loss/RPNLoss/objectness_loss': 2.5444858,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.108131,
 'learning_rate': 0.042708073}
I0123 14:26:38.729179 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007565207,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.5628887,
 'Loss/RPNLoss/objectness_loss': 2.5444858,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.108131,
 'learning_rate': 0.042708073}
INFO:tensorflow:Step 66400 per-step time 0.378s
I0123 14:27:16.495694 140287364614016 model_lib_v2.py:707] Step 66400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001633033,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.9909986,
 'Loss/RPNLoss/objectness_loss': 0.34291345,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.3355451,
 'learning_rate': 0.042480204}
I0123 14:27:16.496026 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001633033,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.9909986,
 'Loss/RPNLoss/objectness_loss': 0.34291345,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.3355451,
 'learning_rate': 0.042480204}
INFO:tensorflow:Step 66500 per-step time 0.373s
I0123 14:27:53.822713 140287364614016 model_lib_v2.py:707] Step 66500 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009110337,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 18.840927,
 'Loss/RPNLoss/objectness_loss': 1.5229821,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.36482,
 'learning_rate': 0.04225275}
I0123 14:27:53.823029 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009110337,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 18.840927,
 'Loss/RPNLoss/objectness_loss': 1.5229821,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.36482,
 'learning_rate': 0.04225275}
INFO:tensorflow:Step 66600 per-step time 0.376s
I0123 14:28:31.392341 140287364614016 model_lib_v2.py:707] Step 66600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008831313,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 186.84494,
 'Loss/RPNLoss/objectness_loss': 1.1450272,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 187.9988,
 'learning_rate': 0.042025667}
I0123 14:28:31.392649 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008831313,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 186.84494,
 'Loss/RPNLoss/objectness_loss': 1.1450272,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 187.9988,
 'learning_rate': 0.042025667}
INFO:tensorflow:Step 66700 per-step time 0.378s
I0123 14:29:09.201788 140287364614016 model_lib_v2.py:707] Step 66700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.90662026,
 'Loss/BoxClassifierLoss/localization_loss': 1.3705763,
 'Loss/RPNLoss/localization_loss': 129.9196,
 'Loss/RPNLoss/objectness_loss': 0.3341232,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 132.53091,
 'learning_rate': 0.041798975}
I0123 14:29:09.202142 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.90662026,
 'Loss/BoxClassifierLoss/localization_loss': 1.3705763,
 'Loss/RPNLoss/localization_loss': 129.9196,
 'Loss/RPNLoss/objectness_loss': 0.3341232,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 132.53091,
 'learning_rate': 0.041798975}
INFO:tensorflow:Step 66800 per-step time 0.378s
I0123 14:29:46.990561 140287364614016 model_lib_v2.py:707] Step 66800 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5758336,
 'Loss/BoxClassifierLoss/localization_loss': 0.37216204,
 'Loss/RPNLoss/localization_loss': 463.5587,
 'Loss/RPNLoss/objectness_loss': 1.7162435,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 466.22293,
 'learning_rate': 0.041572697}
I0123 14:29:46.990869 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5758336,
 'Loss/BoxClassifierLoss/localization_loss': 0.37216204,
 'Loss/RPNLoss/localization_loss': 463.5587,
 'Loss/RPNLoss/objectness_loss': 1.7162435,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 466.22293,
 'learning_rate': 0.041572697}
INFO:tensorflow:Step 66900 per-step time 0.376s
I0123 14:30:24.563529 140287364614016 model_lib_v2.py:707] Step 66900 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001158542,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 151.71655,
 'Loss/RPNLoss/objectness_loss': 1.5667381,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 153.28445,
 'learning_rate': 0.041346807}
I0123 14:30:24.563854 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001158542,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 151.71655,
 'Loss/RPNLoss/objectness_loss': 1.5667381,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 153.28445,
 'learning_rate': 0.041346807}
INFO:tensorflow:Step 67000 per-step time 0.376s
I0123 14:31:02.127937 140287364614016 model_lib_v2.py:707] Step 67000 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007057325,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.30018267,
 'Loss/RPNLoss/objectness_loss': 1.8499525,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1508408,
 'learning_rate': 0.041121315}
I0123 14:31:02.128259 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007057325,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.30018267,
 'Loss/RPNLoss/objectness_loss': 1.8499525,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1508408,
 'learning_rate': 0.041121315}
INFO:tensorflow:Step 67100 per-step time 0.416s
I0123 14:31:43.727134 140287364614016 model_lib_v2.py:707] Step 67100 per-step time 0.416s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.101306155,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.619981,
 'Loss/RPNLoss/objectness_loss': 2.606194,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.3274813,
 'learning_rate': 0.040896244}
I0123 14:31:43.727470 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.101306155,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.619981,
 'Loss/RPNLoss/objectness_loss': 2.606194,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.3274813,
 'learning_rate': 0.040896244}
INFO:tensorflow:Step 67200 per-step time 0.379s
I0123 14:32:21.583734 140287364614016 model_lib_v2.py:707] Step 67200 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.15257522,
 'Loss/BoxClassifierLoss/localization_loss': 0.14359897,
 'Loss/RPNLoss/localization_loss': 2.3606224,
 'Loss/RPNLoss/objectness_loss': 0.055753827,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7125504,
 'learning_rate': 0.04067156}
I0123 14:32:21.584040 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.15257522,
 'Loss/BoxClassifierLoss/localization_loss': 0.14359897,
 'Loss/RPNLoss/localization_loss': 2.3606224,
 'Loss/RPNLoss/objectness_loss': 0.055753827,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7125504,
 'learning_rate': 0.04067156}
INFO:tensorflow:Step 67300 per-step time 0.373s
I0123 14:32:58.889015 140287364614016 model_lib_v2.py:707] Step 67300 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.010589155,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 9.956725,
 'Loss/RPNLoss/objectness_loss': 1.6576607,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 11.624975,
 'learning_rate': 0.040447306}
I0123 14:32:58.889334 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.010589155,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 9.956725,
 'Loss/RPNLoss/objectness_loss': 1.6576607,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 11.624975,
 'learning_rate': 0.040447306}
INFO:tensorflow:Step 67400 per-step time 0.377s
I0123 14:33:36.629634 140287364614016 model_lib_v2.py:707] Step 67400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00045717304,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 190.40985,
 'Loss/RPNLoss/objectness_loss': 4.6447096,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 195.05502,
 'learning_rate': 0.040223427}
I0123 14:33:36.629938 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00045717304,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 190.40985,
 'Loss/RPNLoss/objectness_loss': 4.6447096,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 195.05502,
 'learning_rate': 0.040223427}
INFO:tensorflow:Step 67500 per-step time 0.376s
I0123 14:34:14.225439 140287364614016 model_lib_v2.py:707] Step 67500 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005166128,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.8568997,
 'Loss/RPNLoss/objectness_loss': 0.6456769,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.5030932,
 'learning_rate': 0.039999995}
I0123 14:34:14.225737 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005166128,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.8568997,
 'Loss/RPNLoss/objectness_loss': 0.6456769,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.5030932,
 'learning_rate': 0.039999995}
INFO:tensorflow:Step 67600 per-step time 0.381s
I0123 14:34:52.289035 140287364614016 model_lib_v2.py:707] Step 67600 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016063936,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 89.55681,
 'Loss/RPNLoss/objectness_loss': 0.28165355,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 89.85452,
 'learning_rate': 0.03977696}
I0123 14:34:52.289359 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016063936,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 89.55681,
 'Loss/RPNLoss/objectness_loss': 0.28165355,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 89.85452,
 'learning_rate': 0.03977696}
INFO:tensorflow:Step 67700 per-step time 0.375s
I0123 14:35:29.773698 140287364614016 model_lib_v2.py:707] Step 67700 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.011335067,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.3398994,
 'Loss/RPNLoss/objectness_loss': 0.19628412,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.5475186,
 'learning_rate': 0.03955436}
I0123 14:35:29.774045 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.011335067,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.3398994,
 'Loss/RPNLoss/objectness_loss': 0.19628412,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.5475186,
 'learning_rate': 0.03955436}
INFO:tensorflow:Step 67800 per-step time 0.380s
I0123 14:36:07.801058 140287364614016 model_lib_v2.py:707] Step 67800 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5717468,
 'Loss/BoxClassifierLoss/localization_loss': 0.31318742,
 'Loss/RPNLoss/localization_loss': 0.15776302,
 'Loss/RPNLoss/objectness_loss': 2.709584,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.7522812,
 'learning_rate': 0.03933216}
I0123 14:36:07.801362 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5717468,
 'Loss/BoxClassifierLoss/localization_loss': 0.31318742,
 'Loss/RPNLoss/localization_loss': 0.15776302,
 'Loss/RPNLoss/objectness_loss': 2.709584,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.7522812,
 'learning_rate': 0.03933216}
INFO:tensorflow:Step 67900 per-step time 0.378s
I0123 14:36:45.615117 140287364614016 model_lib_v2.py:707] Step 67900 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.027279086,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.07625271,
 'Loss/RPNLoss/objectness_loss': 5.6926556,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7961874,
 'learning_rate': 0.03911039}
I0123 14:36:45.615448 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.027279086,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.07625271,
 'Loss/RPNLoss/objectness_loss': 5.6926556,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7961874,
 'learning_rate': 0.03911039}
INFO:tensorflow:Step 68000 per-step time 0.375s
I0123 14:37:23.139506 140287364614016 model_lib_v2.py:707] Step 68000 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.04281176,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 81.70731,
 'Loss/RPNLoss/objectness_loss': 1.007916,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 82.75804,
 'learning_rate': 0.038889054}
I0123 14:37:23.139845 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.04281176,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 81.70731,
 'Loss/RPNLoss/objectness_loss': 1.007916,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 82.75804,
 'learning_rate': 0.038889054}
INFO:tensorflow:Step 68100 per-step time 0.414s
I0123 14:38:04.493536 140287364614016 model_lib_v2.py:707] Step 68100 per-step time 0.414s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00050452765,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.790115,
 'Loss/RPNLoss/objectness_loss': 0.8132393,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 105.60386,
 'learning_rate': 0.03866813}
I0123 14:38:04.493857 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00050452765,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.790115,
 'Loss/RPNLoss/objectness_loss': 0.8132393,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 105.60386,
 'learning_rate': 0.03866813}
INFO:tensorflow:Step 68200 per-step time 0.376s
I0123 14:38:42.066681 140287364614016 model_lib_v2.py:707] Step 68200 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013446165,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 38.99012,
 'Loss/RPNLoss/objectness_loss': 1.2091303,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 40.200596,
 'learning_rate': 0.038447637}
I0123 14:38:42.067008 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013446165,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 38.99012,
 'Loss/RPNLoss/objectness_loss': 1.2091303,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 40.200596,
 'learning_rate': 0.038447637}
INFO:tensorflow:Step 68300 per-step time 0.378s
I0123 14:39:19.901208 140287364614016 model_lib_v2.py:707] Step 68300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.002341417,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 190.38118,
 'Loss/RPNLoss/objectness_loss': 2.5381734,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 192.92169,
 'learning_rate': 0.03822759}
I0123 14:39:19.901573 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.002341417,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 190.38118,
 'Loss/RPNLoss/objectness_loss': 2.5381734,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 192.92169,
 'learning_rate': 0.03822759}
INFO:tensorflow:Step 68400 per-step time 0.379s
I0123 14:39:57.781658 140287364614016 model_lib_v2.py:707] Step 68400 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013594169,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.256328,
 'Loss/RPNLoss/objectness_loss': 3.376622,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.63431,
 'learning_rate': 0.03800796}
I0123 14:39:57.781968 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013594169,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.256328,
 'Loss/RPNLoss/objectness_loss': 3.376622,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.63431,
 'learning_rate': 0.03800796}
INFO:tensorflow:Step 68500 per-step time 0.379s
I0123 14:40:35.701738 140287364614016 model_lib_v2.py:707] Step 68500 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0023285174,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.2088563,
 'Loss/RPNLoss/objectness_loss': 5.407342,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.618526,
 'learning_rate': 0.037788767}
I0123 14:40:35.702041 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0023285174,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.2088563,
 'Loss/RPNLoss/objectness_loss': 5.407342,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.618526,
 'learning_rate': 0.037788767}
INFO:tensorflow:Step 68600 per-step time 0.378s
I0123 14:41:13.507974 140287364614016 model_lib_v2.py:707] Step 68600 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00074478844,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.6535865,
 'Loss/RPNLoss/objectness_loss': 0.31132272,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.965654,
 'learning_rate': 0.037570026}
I0123 14:41:13.508322 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00074478844,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.6535865,
 'Loss/RPNLoss/objectness_loss': 0.31132272,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.965654,
 'learning_rate': 0.037570026}
INFO:tensorflow:Step 68700 per-step time 0.378s
I0123 14:41:51.343844 140287364614016 model_lib_v2.py:707] Step 68700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00077436573,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 67.464645,
 'Loss/RPNLoss/objectness_loss': 0.87809336,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 68.34351,
 'learning_rate': 0.037351716}
I0123 14:41:51.344183 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00077436573,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 67.464645,
 'Loss/RPNLoss/objectness_loss': 0.87809336,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 68.34351,
 'learning_rate': 0.037351716}
INFO:tensorflow:Step 68800 per-step time 0.377s
I0123 14:42:29.061659 140287364614016 model_lib_v2.py:707] Step 68800 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0024344341,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 11.448629,
 'Loss/RPNLoss/objectness_loss': 0.62004596,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.07111,
 'learning_rate': 0.03713386}
I0123 14:42:29.061973 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0024344341,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 11.448629,
 'Loss/RPNLoss/objectness_loss': 0.62004596,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.07111,
 'learning_rate': 0.03713386}
INFO:tensorflow:Step 68900 per-step time 0.380s
I0123 14:43:07.058868 140287364614016 model_lib_v2.py:707] Step 68900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.02223115,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.90169,
 'Loss/RPNLoss/objectness_loss': 0.658229,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 70.582146,
 'learning_rate': 0.036916416}
I0123 14:43:07.059190 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.02223115,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 69.90169,
 'Loss/RPNLoss/objectness_loss': 0.658229,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 70.582146,
 'learning_rate': 0.036916416}
INFO:tensorflow:Step 69000 per-step time 0.377s
I0123 14:43:44.740846 140287364614016 model_lib_v2.py:707] Step 69000 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005415403,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.8678,
 'Loss/RPNLoss/objectness_loss': 3.850308,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 108.71865,
 'learning_rate': 0.036699455}
I0123 14:43:44.741152 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005415403,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.8678,
 'Loss/RPNLoss/objectness_loss': 3.850308,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 108.71865,
 'learning_rate': 0.036699455}
INFO:tensorflow:Step 69100 per-step time 0.409s
I0123 14:44:25.689075 140287364614016 model_lib_v2.py:707] Step 69100 per-step time 0.409s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.646467,
 'Loss/BoxClassifierLoss/localization_loss': 0.27335536,
 'Loss/RPNLoss/localization_loss': 213.20052,
 'Loss/RPNLoss/objectness_loss': 0.070837475,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 214.19118,
 'learning_rate': 0.036482926}
I0123 14:44:25.689393 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.646467,
 'Loss/BoxClassifierLoss/localization_loss': 0.27335536,
 'Loss/RPNLoss/localization_loss': 213.20052,
 'Loss/RPNLoss/objectness_loss': 0.070837475,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 214.19118,
 'learning_rate': 0.036482926}
INFO:tensorflow:Step 69200 per-step time 0.384s
I0123 14:45:04.053019 140287364614016 model_lib_v2.py:707] Step 69200 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007440754,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 92.8174,
 'Loss/RPNLoss/objectness_loss': 2.9257832,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.74393,
 'learning_rate': 0.036266863}
I0123 14:45:04.053343 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007440754,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 92.8174,
 'Loss/RPNLoss/objectness_loss': 2.9257832,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.74393,
 'learning_rate': 0.036266863}
INFO:tensorflow:Step 69300 per-step time 0.379s
I0123 14:45:41.936893 140287364614016 model_lib_v2.py:707] Step 69300 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0037911234,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 108.92578,
 'Loss/RPNLoss/objectness_loss': 1.7931037,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 110.72268,
 'learning_rate': 0.03605124}
I0123 14:45:41.937188 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0037911234,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 108.92578,
 'Loss/RPNLoss/objectness_loss': 1.7931037,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 110.72268,
 'learning_rate': 0.03605124}
INFO:tensorflow:Step 69400 per-step time 0.376s
I0123 14:46:19.561093 140287364614016 model_lib_v2.py:707] Step 69400 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018804133,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.710282,
 'Loss/RPNLoss/objectness_loss': 8.5800085,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.309095,
 'learning_rate': 0.03583609}
I0123 14:46:19.561381 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018804133,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.710282,
 'Loss/RPNLoss/objectness_loss': 8.5800085,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.309095,
 'learning_rate': 0.03583609}
INFO:tensorflow:Step 69500 per-step time 0.381s
I0123 14:46:57.667685 140287364614016 model_lib_v2.py:707] Step 69500 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00054142124,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.6460152,
 'Loss/RPNLoss/objectness_loss': 6.075554,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.7221103,
 'learning_rate': 0.03562138}
I0123 14:46:57.668014 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00054142124,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.6460152,
 'Loss/RPNLoss/objectness_loss': 6.075554,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.7221103,
 'learning_rate': 0.03562138}
INFO:tensorflow:Step 69600 per-step time 0.377s
I0123 14:47:35.402656 140287364614016 model_lib_v2.py:707] Step 69600 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00054321403,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 135.05266,
 'Loss/RPNLoss/objectness_loss': 0.571265,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 135.62447,
 'learning_rate': 0.035407133}
I0123 14:47:35.402967 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00054321403,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 135.05266,
 'Loss/RPNLoss/objectness_loss': 0.571265,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 135.62447,
 'learning_rate': 0.035407133}
INFO:tensorflow:Step 69700 per-step time 0.377s
I0123 14:48:13.134203 140287364614016 model_lib_v2.py:707] Step 69700 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0006714335,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 5.8901715,
 'Loss/RPNLoss/objectness_loss': 2.0861309,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.9769735,
 'learning_rate': 0.035193346}
I0123 14:48:13.134526 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0006714335,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 5.8901715,
 'Loss/RPNLoss/objectness_loss': 2.0861309,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.9769735,
 'learning_rate': 0.035193346}
INFO:tensorflow:Step 69800 per-step time 0.378s
I0123 14:48:50.894316 140287364614016 model_lib_v2.py:707] Step 69800 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.012285953,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.064982906,
 'Loss/RPNLoss/objectness_loss': 1.0114379,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.0887067,
 'learning_rate': 0.034980044}
I0123 14:48:50.894630 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.012285953,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.064982906,
 'Loss/RPNLoss/objectness_loss': 1.0114379,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.0887067,
 'learning_rate': 0.034980044}
INFO:tensorflow:Step 69900 per-step time 0.377s
I0123 14:49:28.627942 140287364614016 model_lib_v2.py:707] Step 69900 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.04658323,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 96.22293,
 'Loss/RPNLoss/objectness_loss': 0.13149741,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 96.40101,
 'learning_rate': 0.034767188}
I0123 14:49:28.628266 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.04658323,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 96.22293,
 'Loss/RPNLoss/objectness_loss': 0.13149741,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 96.40101,
 'learning_rate': 0.034767188}
INFO:tensorflow:Step 70000 per-step time 0.378s
I0123 14:50:06.423958 140287364614016 model_lib_v2.py:707] Step 70000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005913003,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.9126196,
 'Loss/RPNLoss/objectness_loss': 1.958952,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.872163,
 'learning_rate': 0.034554806}
I0123 14:50:06.424284 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005913003,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.9126196,
 'Loss/RPNLoss/objectness_loss': 1.958952,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.872163,
 'learning_rate': 0.034554806}
INFO:tensorflow:Step 70100 per-step time 0.411s
I0123 14:50:47.543917 140287364614016 model_lib_v2.py:707] Step 70100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00530028,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 154.0235,
 'Loss/RPNLoss/objectness_loss': 1.7925503,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 155.82135,
 'learning_rate': 0.03434291}
I0123 14:50:47.544255 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00530028,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 154.0235,
 'Loss/RPNLoss/objectness_loss': 1.7925503,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 155.82135,
 'learning_rate': 0.03434291}
INFO:tensorflow:Step 70200 per-step time 0.373s
I0123 14:51:24.872925 140287364614016 model_lib_v2.py:707] Step 70200 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00088127406,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.19978368,
 'Loss/RPNLoss/objectness_loss': 1.6303716,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.8310366,
 'learning_rate': 0.034131475}
I0123 14:51:24.873330 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00088127406,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.19978368,
 'Loss/RPNLoss/objectness_loss': 1.6303716,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.8310366,
 'learning_rate': 0.034131475}
INFO:tensorflow:Step 70300 per-step time 0.374s
I0123 14:52:02.264473 140287364614016 model_lib_v2.py:707] Step 70300 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013589845,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 77.718056,
 'Loss/RPNLoss/objectness_loss': 0.53533536,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 78.25475,
 'learning_rate': 0.03392053}
I0123 14:52:02.264772 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013589845,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 77.718056,
 'Loss/RPNLoss/objectness_loss': 0.53533536,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 78.25475,
 'learning_rate': 0.03392053}
INFO:tensorflow:Step 70400 per-step time 0.372s
I0123 14:52:39.455614 140287364614016 model_lib_v2.py:707] Step 70400 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00062303204,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 72.64623,
 'Loss/RPNLoss/objectness_loss': 0.23421729,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 72.88107,
 'learning_rate': 0.033710044}
I0123 14:52:39.455928 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00062303204,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 72.64623,
 'Loss/RPNLoss/objectness_loss': 0.23421729,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 72.88107,
 'learning_rate': 0.033710044}
INFO:tensorflow:Step 70500 per-step time 0.377s
I0123 14:53:17.178638 140287364614016 model_lib_v2.py:707] Step 70500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0057256985,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 167.31453,
 'Loss/RPNLoss/objectness_loss': 4.22501,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 171.54526,
 'learning_rate': 0.033500046}
I0123 14:53:17.178962 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0057256985,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 167.31453,
 'Loss/RPNLoss/objectness_loss': 4.22501,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 171.54526,
 'learning_rate': 0.033500046}
INFO:tensorflow:Step 70600 per-step time 0.373s
I0123 14:53:54.508365 140287364614016 model_lib_v2.py:707] Step 70600 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005274398,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 110.11534,
 'Loss/RPNLoss/objectness_loss': 4.392782,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 114.50865,
 'learning_rate': 0.033290528}
I0123 14:53:54.508695 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005274398,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 110.11534,
 'Loss/RPNLoss/objectness_loss': 4.392782,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 114.50865,
 'learning_rate': 0.033290528}
INFO:tensorflow:Step 70700 per-step time 0.373s
I0123 14:54:31.834865 140287364614016 model_lib_v2.py:707] Step 70700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0006035818,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 7.2183175,
 'Loss/RPNLoss/objectness_loss': 7.616486,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 14.835407,
 'learning_rate': 0.033081505}
I0123 14:54:31.835172 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0006035818,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 7.2183175,
 'Loss/RPNLoss/objectness_loss': 7.616486,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 14.835407,
 'learning_rate': 0.033081505}
INFO:tensorflow:Step 70800 per-step time 0.379s
I0123 14:55:09.689151 140287364614016 model_lib_v2.py:707] Step 70800 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.021801695,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 32.38252,
 'Loss/RPNLoss/objectness_loss': 0.65434825,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 33.05867,
 'learning_rate': 0.032872964}
I0123 14:55:09.689477 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.021801695,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 32.38252,
 'Loss/RPNLoss/objectness_loss': 0.65434825,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 33.05867,
 'learning_rate': 0.032872964}
INFO:tensorflow:Step 70900 per-step time 0.375s
I0123 14:55:47.178351 140287364614016 model_lib_v2.py:707] Step 70900 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00084949925,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1335633,
 'Loss/RPNLoss/objectness_loss': 0.8482764,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.9826891,
 'learning_rate': 0.032664917}
I0123 14:55:47.178681 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00084949925,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.1335633,
 'Loss/RPNLoss/objectness_loss': 0.8482764,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.9826891,
 'learning_rate': 0.032664917}
INFO:tensorflow:Step 71000 per-step time 0.377s
I0123 14:56:24.918713 140287364614016 model_lib_v2.py:707] Step 71000 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.053898003,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 72.32569,
 'Loss/RPNLoss/objectness_loss': 0.62803006,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.00762,
 'learning_rate': 0.03245735}
I0123 14:56:24.919061 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.053898003,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 72.32569,
 'Loss/RPNLoss/objectness_loss': 0.62803006,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.00762,
 'learning_rate': 0.03245735}
INFO:tensorflow:Step 71100 per-step time 0.406s
I0123 14:57:05.482297 140287364614016 model_lib_v2.py:707] Step 71100 per-step time 0.406s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009091376,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.71598,
 'Loss/RPNLoss/objectness_loss': 0.22815001,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 53.945038,
 'learning_rate': 0.03225028}
I0123 14:57:05.482633 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009091376,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.71598,
 'Loss/RPNLoss/objectness_loss': 0.22815001,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 53.945038,
 'learning_rate': 0.03225028}
INFO:tensorflow:Step 71200 per-step time 0.382s
I0123 14:57:43.635603 140287364614016 model_lib_v2.py:707] Step 71200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00051253196,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.4139921,
 'Loss/RPNLoss/objectness_loss': 0.69627976,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.1107844,
 'learning_rate': 0.032043703}
I0123 14:57:43.635941 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00051253196,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.4139921,
 'Loss/RPNLoss/objectness_loss': 0.69627976,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.1107844,
 'learning_rate': 0.032043703}
INFO:tensorflow:Step 71300 per-step time 0.375s
I0123 14:58:21.142415 140287364614016 model_lib_v2.py:707] Step 71300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00063346035,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 25.288792,
 'Loss/RPNLoss/objectness_loss': 1.7235895,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 27.013014,
 'learning_rate': 0.03183764}
I0123 14:58:21.142708 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00063346035,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 25.288792,
 'Loss/RPNLoss/objectness_loss': 1.7235895,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 27.013014,
 'learning_rate': 0.03183764}
INFO:tensorflow:Step 71400 per-step time 0.375s
I0123 14:58:58.675825 140287364614016 model_lib_v2.py:707] Step 71400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.12436466,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 105.56708,
 'Loss/RPNLoss/objectness_loss': 1.8813133,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 107.572754,
 'learning_rate': 0.031632062}
I0123 14:58:58.676180 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.12436466,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 105.56708,
 'Loss/RPNLoss/objectness_loss': 1.8813133,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 107.572754,
 'learning_rate': 0.031632062}
INFO:tensorflow:Step 71500 per-step time 0.380s
I0123 14:59:36.655242 140287364614016 model_lib_v2.py:707] Step 71500 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.9116488,
 'Loss/BoxClassifierLoss/localization_loss': 0.8238957,
 'Loss/RPNLoss/localization_loss': 105.023605,
 'Loss/RPNLoss/objectness_loss': 0.4941777,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 107.25333,
 'learning_rate': 0.03142699}
I0123 14:59:36.655540 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.9116488,
 'Loss/BoxClassifierLoss/localization_loss': 0.8238957,
 'Loss/RPNLoss/localization_loss': 105.023605,
 'Loss/RPNLoss/objectness_loss': 0.4941777,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 107.25333,
 'learning_rate': 0.03142699}
INFO:tensorflow:Step 71600 per-step time 0.383s
I0123 15:00:14.972617 140287364614016 model_lib_v2.py:707] Step 71600 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1618368,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 61.20216,
 'Loss/RPNLoss/objectness_loss': 2.7929182,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 64.15691,
 'learning_rate': 0.03122243}
I0123 15:00:14.972942 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1618368,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 61.20216,
 'Loss/RPNLoss/objectness_loss': 2.7929182,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 64.15691,
 'learning_rate': 0.03122243}
INFO:tensorflow:Step 71700 per-step time 0.381s
I0123 15:00:53.110972 140287364614016 model_lib_v2.py:707] Step 71700 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.4398145,
 'Loss/BoxClassifierLoss/localization_loss': 0.3579299,
 'Loss/RPNLoss/localization_loss': 6.831536,
 'Loss/RPNLoss/objectness_loss': 0.18332453,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.812605,
 'learning_rate': 0.031018361}
I0123 15:00:53.111315 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.4398145,
 'Loss/BoxClassifierLoss/localization_loss': 0.3579299,
 'Loss/RPNLoss/localization_loss': 6.831536,
 'Loss/RPNLoss/objectness_loss': 0.18332453,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.812605,
 'learning_rate': 0.031018361}
INFO:tensorflow:Step 71800 per-step time 0.379s
I0123 15:01:31.024224 140287364614016 model_lib_v2.py:707] Step 71800 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.039415125,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 18.566307,
 'Loss/RPNLoss/objectness_loss': 1.8501338,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.455856,
 'learning_rate': 0.030814819}
I0123 15:01:31.024549 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.039415125,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 18.566307,
 'Loss/RPNLoss/objectness_loss': 1.8501338,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.455856,
 'learning_rate': 0.030814819}
INFO:tensorflow:Step 71900 per-step time 0.381s
I0123 15:02:09.087195 140287364614016 model_lib_v2.py:707] Step 71900 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.06524037,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.12228,
 'Loss/RPNLoss/objectness_loss': 5.6834393,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 109.87096,
 'learning_rate': 0.030611772}
I0123 15:02:09.087499 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.06524037,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 104.12228,
 'Loss/RPNLoss/objectness_loss': 5.6834393,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 109.87096,
 'learning_rate': 0.030611772}
INFO:tensorflow:Step 72000 per-step time 0.378s
I0123 15:02:46.920610 140287364614016 model_lib_v2.py:707] Step 72000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.38183287,
 'Loss/BoxClassifierLoss/localization_loss': 0.48898983,
 'Loss/RPNLoss/localization_loss': 78.6706,
 'Loss/RPNLoss/objectness_loss': 3.2179904,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 82.759415,
 'learning_rate': 0.03040924}
I0123 15:02:46.920947 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.38183287,
 'Loss/BoxClassifierLoss/localization_loss': 0.48898983,
 'Loss/RPNLoss/localization_loss': 78.6706,
 'Loss/RPNLoss/objectness_loss': 3.2179904,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 82.759415,
 'learning_rate': 0.03040924}
INFO:tensorflow:Step 72100 per-step time 0.406s
I0123 15:03:27.554902 140287364614016 model_lib_v2.py:707] Step 72100 per-step time 0.406s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00082473876,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 88.6578,
 'Loss/RPNLoss/objectness_loss': 0.47007427,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 89.1287,
 'learning_rate': 0.030207219}
I0123 15:03:27.555239 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00082473876,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 88.6578,
 'Loss/RPNLoss/objectness_loss': 0.47007427,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 89.1287,
 'learning_rate': 0.030207219}
INFO:tensorflow:Step 72200 per-step time 0.377s
I0123 15:04:05.281454 140287364614016 model_lib_v2.py:707] Step 72200 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.011326542,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 99.24808,
 'Loss/RPNLoss/objectness_loss': 2.7507653,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 102.01017,
 'learning_rate': 0.03000573}
I0123 15:04:05.281760 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.011326542,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 99.24808,
 'Loss/RPNLoss/objectness_loss': 2.7507653,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 102.01017,
 'learning_rate': 0.03000573}
INFO:tensorflow:Step 72300 per-step time 0.375s
I0123 15:04:42.763705 140287364614016 model_lib_v2.py:707] Step 72300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000346513,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 7.0507717,
 'Loss/RPNLoss/objectness_loss': 1.4530802,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.504198,
 'learning_rate': 0.02980475}
I0123 15:04:42.764028 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000346513,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 7.0507717,
 'Loss/RPNLoss/objectness_loss': 1.4530802,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.504198,
 'learning_rate': 0.02980475}
INFO:tensorflow:Step 72400 per-step time 0.375s
I0123 15:05:20.271388 140287364614016 model_lib_v2.py:707] Step 72400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.026922105,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.29329443,
 'Loss/RPNLoss/objectness_loss': 1.9307644,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.2509809,
 'learning_rate': 0.0296043}
I0123 15:05:20.271713 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.026922105,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.29329443,
 'Loss/RPNLoss/objectness_loss': 1.9307644,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.2509809,
 'learning_rate': 0.0296043}
INFO:tensorflow:Step 72500 per-step time 0.378s
I0123 15:05:58.045071 140287364614016 model_lib_v2.py:707] Step 72500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3987422,
 'Loss/BoxClassifierLoss/localization_loss': 0.6076676,
 'Loss/RPNLoss/localization_loss': 169.30276,
 'Loss/RPNLoss/objectness_loss': 1.582552,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 172.89172,
 'learning_rate': 0.029404363}
I0123 15:05:58.045361 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3987422,
 'Loss/BoxClassifierLoss/localization_loss': 0.6076676,
 'Loss/RPNLoss/localization_loss': 169.30276,
 'Loss/RPNLoss/objectness_loss': 1.582552,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 172.89172,
 'learning_rate': 0.029404363}
INFO:tensorflow:Step 72600 per-step time 0.382s
I0123 15:06:36.228900 140287364614016 model_lib_v2.py:707] Step 72600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.29784897,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.8476028,
 'Loss/RPNLoss/objectness_loss': 3.1605659,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.306018,
 'learning_rate': 0.02920495}
I0123 15:06:36.229202 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.29784897,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.8476028,
 'Loss/RPNLoss/objectness_loss': 3.1605659,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.306018,
 'learning_rate': 0.02920495}
INFO:tensorflow:Step 72700 per-step time 0.383s
I0123 15:07:14.501091 140287364614016 model_lib_v2.py:707] Step 72700 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.012026801,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 12.4285,
 'Loss/RPNLoss/objectness_loss': 0.22433713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.664865,
 'learning_rate': 0.02900608}
I0123 15:07:14.501429 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.012026801,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 12.4285,
 'Loss/RPNLoss/objectness_loss': 0.22433713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 12.664865,
 'learning_rate': 0.02900608}
INFO:tensorflow:Step 72800 per-step time 0.381s
I0123 15:07:52.568845 140287364614016 model_lib_v2.py:707] Step 72800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0021170634,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.0506619,
 'Loss/RPNLoss/objectness_loss': 0.076780505,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.1295595,
 'learning_rate': 0.028807726}
I0123 15:07:52.569163 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0021170634,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.0506619,
 'Loss/RPNLoss/objectness_loss': 0.076780505,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.1295595,
 'learning_rate': 0.028807726}
INFO:tensorflow:Step 72900 per-step time 0.384s
I0123 15:08:30.936770 140287364614016 model_lib_v2.py:707] Step 72900 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1757136,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.46627,
 'Loss/RPNLoss/objectness_loss': 0.74252445,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 54.38451,
 'learning_rate': 0.0286099}
I0123 15:08:30.937078 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1757136,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.46627,
 'Loss/RPNLoss/objectness_loss': 0.74252445,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 54.38451,
 'learning_rate': 0.0286099}
INFO:tensorflow:Step 73000 per-step time 0.378s
I0123 15:09:08.711704 140287364614016 model_lib_v2.py:707] Step 73000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0067836014,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.33196327,
 'Loss/RPNLoss/objectness_loss': 2.36171,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7004569,
 'learning_rate': 0.028412623}
I0123 15:09:08.712028 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0067836014,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.33196327,
 'Loss/RPNLoss/objectness_loss': 2.36171,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7004569,
 'learning_rate': 0.028412623}
INFO:tensorflow:Step 73100 per-step time 0.413s
I0123 15:09:50.024610 140287364614016 model_lib_v2.py:707] Step 73100 per-step time 0.413s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009526922,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 73.465996,
 'Loss/RPNLoss/objectness_loss': 0.33659932,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.80355,
 'learning_rate': 0.028215865}
I0123 15:09:50.024907 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009526922,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 73.465996,
 'Loss/RPNLoss/objectness_loss': 0.33659932,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 73.80355,
 'learning_rate': 0.028215865}
INFO:tensorflow:Step 73200 per-step time 0.383s
I0123 15:10:28.358498 140287364614016 model_lib_v2.py:707] Step 73200 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.24346104,
 'Loss/BoxClassifierLoss/localization_loss': 0.102367304,
 'Loss/RPNLoss/localization_loss': 0.470524,
 'Loss/RPNLoss/objectness_loss': 6.8945785,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.710931,
 'learning_rate': 0.028019646}
I0123 15:10:28.358799 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.24346104,
 'Loss/BoxClassifierLoss/localization_loss': 0.102367304,
 'Loss/RPNLoss/localization_loss': 0.470524,
 'Loss/RPNLoss/objectness_loss': 6.8945785,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 7.710931,
 'learning_rate': 0.028019646}
INFO:tensorflow:Step 73300 per-step time 0.379s
I0123 15:11:06.283710 140287364614016 model_lib_v2.py:707] Step 73300 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.7973008,
 'Loss/BoxClassifierLoss/localization_loss': 0.9135337,
 'Loss/RPNLoss/localization_loss': 94.00036,
 'Loss/RPNLoss/objectness_loss': 4.513314,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 100.22451,
 'learning_rate': 0.02782398}
I0123 15:11:06.284035 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.7973008,
 'Loss/BoxClassifierLoss/localization_loss': 0.9135337,
 'Loss/RPNLoss/localization_loss': 94.00036,
 'Loss/RPNLoss/objectness_loss': 4.513314,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 100.22451,
 'learning_rate': 0.02782398}
INFO:tensorflow:Step 73400 per-step time 0.380s
I0123 15:11:44.279416 140287364614016 model_lib_v2.py:707] Step 73400 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3891243,
 'Loss/BoxClassifierLoss/localization_loss': 0.8228241,
 'Loss/RPNLoss/localization_loss': 127.63491,
 'Loss/RPNLoss/objectness_loss': 0.6003491,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 130.4472,
 'learning_rate': 0.027628846}
I0123 15:11:44.279750 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3891243,
 'Loss/BoxClassifierLoss/localization_loss': 0.8228241,
 'Loss/RPNLoss/localization_loss': 127.63491,
 'Loss/RPNLoss/objectness_loss': 0.6003491,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 130.4472,
 'learning_rate': 0.027628846}
INFO:tensorflow:Step 73500 per-step time 0.377s
I0123 15:12:21.947611 140287364614016 model_lib_v2.py:707] Step 73500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00087223866,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.0859091,
 'Loss/RPNLoss/objectness_loss': 1.1829796,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.269761,
 'learning_rate': 0.027434267}
I0123 15:12:21.947959 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00087223866,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.0859091,
 'Loss/RPNLoss/objectness_loss': 1.1829796,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.269761,
 'learning_rate': 0.027434267}
INFO:tensorflow:Step 73600 per-step time 0.382s
I0123 15:13:00.182371 140287364614016 model_lib_v2.py:707] Step 73600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.014166585,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 160.85037,
 'Loss/RPNLoss/objectness_loss': 0.17536244,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 161.0399,
 'learning_rate': 0.02724021}
I0123 15:13:00.182695 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.014166585,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 160.85037,
 'Loss/RPNLoss/objectness_loss': 0.17536244,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 161.0399,
 'learning_rate': 0.02724021}
INFO:tensorflow:Step 73700 per-step time 0.379s
I0123 15:13:38.050254 140287364614016 model_lib_v2.py:707] Step 73700 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00043680478,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.2637485,
 'Loss/RPNLoss/objectness_loss': 0.7873463,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.0515316,
 'learning_rate': 0.027046723}
I0123 15:13:38.050566 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00043680478,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.2637485,
 'Loss/RPNLoss/objectness_loss': 0.7873463,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.0515316,
 'learning_rate': 0.027046723}
INFO:tensorflow:Step 73800 per-step time 0.376s
I0123 15:14:15.659247 140287364614016 model_lib_v2.py:707] Step 73800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0518392,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.24848199,
 'Loss/RPNLoss/objectness_loss': 5.452464,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7527857,
 'learning_rate': 0.02685377}
I0123 15:14:15.659525 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0518392,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.24848199,
 'Loss/RPNLoss/objectness_loss': 5.452464,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7527857,
 'learning_rate': 0.02685377}
INFO:tensorflow:Step 73900 per-step time 0.372s
I0123 15:14:52.842096 140287364614016 model_lib_v2.py:707] Step 73900 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00036071072,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 132.65652,
 'Loss/RPNLoss/objectness_loss': 1.7085826,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 134.36546,
 'learning_rate': 0.02666139}
I0123 15:14:52.842406 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00036071072,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 132.65652,
 'Loss/RPNLoss/objectness_loss': 1.7085826,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 134.36546,
 'learning_rate': 0.02666139}
INFO:tensorflow:Step 74000 per-step time 0.374s
I0123 15:15:30.273367 140287364614016 model_lib_v2.py:707] Step 74000 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0051634186,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 79.47951,
 'Loss/RPNLoss/objectness_loss': 1.3588843,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 80.84355,
 'learning_rate': 0.026469545}
I0123 15:15:30.273694 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0051634186,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 79.47951,
 'Loss/RPNLoss/objectness_loss': 1.3588843,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 80.84355,
 'learning_rate': 0.026469545}
INFO:tensorflow:Step 74100 per-step time 0.414s
I0123 15:16:11.641824 140287364614016 model_lib_v2.py:707] Step 74100 per-step time 0.414s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5315701,
 'Loss/BoxClassifierLoss/localization_loss': 1.0494521,
 'Loss/RPNLoss/localization_loss': 81.81295,
 'Loss/RPNLoss/objectness_loss': 0.74107355,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.13505,
 'learning_rate': 0.026278257}
I0123 15:16:11.642187 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5315701,
 'Loss/BoxClassifierLoss/localization_loss': 1.0494521,
 'Loss/RPNLoss/localization_loss': 81.81295,
 'Loss/RPNLoss/objectness_loss': 0.74107355,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.13505,
 'learning_rate': 0.026278257}
INFO:tensorflow:Step 74200 per-step time 0.382s
I0123 15:16:49.830206 140287364614016 model_lib_v2.py:707] Step 74200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.02313353,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 122.058174,
 'Loss/RPNLoss/objectness_loss': 1.8267165,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 123.90803,
 'learning_rate': 0.026087541}
I0123 15:16:49.830522 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.02313353,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 122.058174,
 'Loss/RPNLoss/objectness_loss': 1.8267165,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 123.90803,
 'learning_rate': 0.026087541}
INFO:tensorflow:Step 74300 per-step time 0.378s
I0123 15:17:27.606276 140287364614016 model_lib_v2.py:707] Step 74300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.69090396,
 'Loss/BoxClassifierLoss/localization_loss': 0.1614978,
 'Loss/RPNLoss/localization_loss': 139.08557,
 'Loss/RPNLoss/objectness_loss': 0.5221657,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 140.46014,
 'learning_rate': 0.025897369}
I0123 15:17:27.606603 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.69090396,
 'Loss/BoxClassifierLoss/localization_loss': 0.1614978,
 'Loss/RPNLoss/localization_loss': 139.08557,
 'Loss/RPNLoss/objectness_loss': 0.5221657,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 140.46014,
 'learning_rate': 0.025897369}
INFO:tensorflow:Step 74400 per-step time 0.381s
I0123 15:18:05.679470 140287364614016 model_lib_v2.py:707] Step 74400 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.58643967,
 'Loss/BoxClassifierLoss/localization_loss': 0.6513663,
 'Loss/RPNLoss/localization_loss': 81.93197,
 'Loss/RPNLoss/objectness_loss': 0.45067203,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 83.620445,
 'learning_rate': 0.025707759}
I0123 15:18:05.679760 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.58643967,
 'Loss/BoxClassifierLoss/localization_loss': 0.6513663,
 'Loss/RPNLoss/localization_loss': 81.93197,
 'Loss/RPNLoss/objectness_loss': 0.45067203,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 83.620445,
 'learning_rate': 0.025707759}
INFO:tensorflow:Step 74500 per-step time 0.381s
I0123 15:18:43.730024 140287364614016 model_lib_v2.py:707] Step 74500 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00080542394,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 116.04094,
 'Loss/RPNLoss/objectness_loss': 1.9837451,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 118.02549,
 'learning_rate': 0.025518727}
I0123 15:18:43.730333 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00080542394,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 116.04094,
 'Loss/RPNLoss/objectness_loss': 1.9837451,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 118.02549,
 'learning_rate': 0.025518727}
INFO:tensorflow:Step 74600 per-step time 0.380s
I0123 15:19:21.683789 140287364614016 model_lib_v2.py:707] Step 74600 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0037135042,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 101.23477,
 'Loss/RPNLoss/objectness_loss': 3.608337,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 104.846825,
 'learning_rate': 0.025330247}
I0123 15:19:21.684133 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0037135042,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 101.23477,
 'Loss/RPNLoss/objectness_loss': 3.608337,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 104.846825,
 'learning_rate': 0.025330247}
INFO:tensorflow:Step 74700 per-step time 0.379s
I0123 15:19:59.610596 140287364614016 model_lib_v2.py:707] Step 74700 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.003726949,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.91364,
 'Loss/RPNLoss/objectness_loss': 2.1933343,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 69.1107,
 'learning_rate': 0.025142334}
I0123 15:19:59.610936 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.003726949,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.91364,
 'Loss/RPNLoss/objectness_loss': 2.1933343,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 69.1107,
 'learning_rate': 0.025142334}
INFO:tensorflow:Step 74800 per-step time 0.384s
I0123 15:20:37.979602 140287364614016 model_lib_v2.py:707] Step 74800 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3525428,
 'Loss/BoxClassifierLoss/localization_loss': 0.49425453,
 'Loss/RPNLoss/localization_loss': 41.610542,
 'Loss/RPNLoss/objectness_loss': 0.811298,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 44.26864,
 'learning_rate': 0.02495501}
I0123 15:20:37.979953 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3525428,
 'Loss/BoxClassifierLoss/localization_loss': 0.49425453,
 'Loss/RPNLoss/localization_loss': 41.610542,
 'Loss/RPNLoss/objectness_loss': 0.811298,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 44.26864,
 'learning_rate': 0.02495501}
INFO:tensorflow:Step 74900 per-step time 0.379s
I0123 15:21:15.877475 140287364614016 model_lib_v2.py:707] Step 74900 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005020907,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 31.087315,
 'Loss/RPNLoss/objectness_loss': 0.96646076,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.05428,
 'learning_rate': 0.024768237}
I0123 15:21:15.877801 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005020907,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 31.087315,
 'Loss/RPNLoss/objectness_loss': 0.96646076,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.05428,
 'learning_rate': 0.024768237}
INFO:tensorflow:Step 75000 per-step time 0.378s
I0123 15:21:53.708402 140287364614016 model_lib_v2.py:707] Step 75000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 2.0565448,
 'Loss/BoxClassifierLoss/localization_loss': 1.534173,
 'Loss/RPNLoss/localization_loss': 21.930069,
 'Loss/RPNLoss/objectness_loss': 0.49400648,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 26.014793,
 'learning_rate': 0.024582053}
I0123 15:21:53.708717 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 2.0565448,
 'Loss/BoxClassifierLoss/localization_loss': 1.534173,
 'Loss/RPNLoss/localization_loss': 21.930069,
 'Loss/RPNLoss/objectness_loss': 0.49400648,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 26.014793,
 'learning_rate': 0.024582053}
INFO:tensorflow:Step 75100 per-step time 0.411s
I0123 15:22:34.848655 140287364614016 model_lib_v2.py:707] Step 75100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0030693708,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 102.17622,
 'Loss/RPNLoss/objectness_loss': 0.417611,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 102.5969,
 'learning_rate': 0.02439642}
I0123 15:22:34.848964 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0030693708,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 102.17622,
 'Loss/RPNLoss/objectness_loss': 0.417611,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 102.5969,
 'learning_rate': 0.02439642}
INFO:tensorflow:Step 75200 per-step time 0.387s
I0123 15:23:13.514364 140287364614016 model_lib_v2.py:707] Step 75200 per-step time 0.387s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3465189,
 'Loss/BoxClassifierLoss/localization_loss': 0.30124843,
 'Loss/RPNLoss/localization_loss': 116.858185,
 'Loss/RPNLoss/objectness_loss': 1.5563141,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 120.06226,
 'learning_rate': 0.024211386}
I0123 15:23:13.514681 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3465189,
 'Loss/BoxClassifierLoss/localization_loss': 0.30124843,
 'Loss/RPNLoss/localization_loss': 116.858185,
 'Loss/RPNLoss/objectness_loss': 1.5563141,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 120.06226,
 'learning_rate': 0.024211386}
INFO:tensorflow:Step 75300 per-step time 0.380s
I0123 15:23:51.480871 140287364614016 model_lib_v2.py:707] Step 75300 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00086909154,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 64.00852,
 'Loss/RPNLoss/objectness_loss': 0.23152813,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 64.24092,
 'learning_rate': 0.024026923}
I0123 15:23:51.481171 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00086909154,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 64.00852,
 'Loss/RPNLoss/objectness_loss': 0.23152813,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 64.24092,
 'learning_rate': 0.024026923}
INFO:tensorflow:Step 75400 per-step time 0.379s
I0123 15:24:29.338495 140287364614016 model_lib_v2.py:707] Step 75400 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6073918,
 'Loss/BoxClassifierLoss/localization_loss': 1.1470118,
 'Loss/RPNLoss/localization_loss': 18.412388,
 'Loss/RPNLoss/objectness_loss': 0.15155664,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 21.318348,
 'learning_rate': 0.02384305}
I0123 15:24:29.338837 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6073918,
 'Loss/BoxClassifierLoss/localization_loss': 1.1470118,
 'Loss/RPNLoss/localization_loss': 18.412388,
 'Loss/RPNLoss/objectness_loss': 0.15155664,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 21.318348,
 'learning_rate': 0.02384305}
INFO:tensorflow:Step 75500 per-step time 0.377s
I0123 15:25:07.035294 140287364614016 model_lib_v2.py:707] Step 75500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00036887912,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 175.6591,
 'Loss/RPNLoss/objectness_loss': 0.29448605,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 175.95396,
 'learning_rate': 0.023659749}
I0123 15:25:07.035643 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00036887912,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 175.6591,
 'Loss/RPNLoss/objectness_loss': 0.29448605,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 175.95396,
 'learning_rate': 0.023659749}
INFO:tensorflow:Step 75600 per-step time 0.379s
I0123 15:25:44.893082 140287364614016 model_lib_v2.py:707] Step 75600 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005750695,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.057603806,
 'Loss/RPNLoss/objectness_loss': 0.554935,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.6131139,
 'learning_rate': 0.023477048}
I0123 15:25:44.893386 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005750695,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.057603806,
 'Loss/RPNLoss/objectness_loss': 0.554935,
 'Loss/regularization_loss': 0.0,
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 'learning_rate': 0.023477048}
INFO:tensorflow:Step 75700 per-step time 0.380s
I0123 15:26:22.903502 140287364614016 model_lib_v2.py:707] Step 75700 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6722314,
 'Loss/BoxClassifierLoss/localization_loss': 0.9127158,
 'Loss/RPNLoss/localization_loss': 38.15146,
 'Loss/RPNLoss/objectness_loss': 0.24437612,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 40.98078,
 'learning_rate': 0.023294916}
I0123 15:26:22.903827 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6722314,
 'Loss/BoxClassifierLoss/localization_loss': 0.9127158,
 'Loss/RPNLoss/localization_loss': 38.15146,
 'Loss/RPNLoss/objectness_loss': 0.24437612,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 40.98078,
 'learning_rate': 0.023294916}
INFO:tensorflow:Step 75800 per-step time 0.381s
I0123 15:27:01.018352 140287364614016 model_lib_v2.py:707] Step 75800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008517849,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.389163,
 'Loss/RPNLoss/objectness_loss': 0.64668775,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.0443687,
 'learning_rate': 0.02311338}
I0123 15:27:01.018673 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008517849,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 4.389163,
 'Loss/RPNLoss/objectness_loss': 0.64668775,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.0443687,
 'learning_rate': 0.02311338}
INFO:tensorflow:Step 75900 per-step time 0.380s
I0123 15:27:38.978788 140287364614016 model_lib_v2.py:707] Step 75900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00030544394,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 84.35978,
 'Loss/RPNLoss/objectness_loss': 0.88528794,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.24537,
 'learning_rate': 0.022932423}
I0123 15:27:38.979098 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00030544394,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 84.35978,
 'Loss/RPNLoss/objectness_loss': 0.88528794,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.24537,
 'learning_rate': 0.022932423}
INFO:tensorflow:Step 76000 per-step time 0.380s
I0123 15:28:16.993444 140287364614016 model_lib_v2.py:707] Step 76000 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00058677385,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.2238488,
 'Loss/RPNLoss/objectness_loss': 0.96406686,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1885023,
 'learning_rate': 0.022752084}
I0123 15:28:16.993769 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00058677385,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.2238488,
 'Loss/RPNLoss/objectness_loss': 0.96406686,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1885023,
 'learning_rate': 0.022752084}
INFO:tensorflow:Step 76100 per-step time 0.417s
I0123 15:28:58.720440 140287364614016 model_lib_v2.py:707] Step 76100 per-step time 0.417s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0022513312,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.5896965,
 'Loss/RPNLoss/objectness_loss': 0.6939055,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.2858534,
 'learning_rate': 0.022572322}
I0123 15:28:58.720772 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0022513312,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.5896965,
 'Loss/RPNLoss/objectness_loss': 0.6939055,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.2858534,
 'learning_rate': 0.022572322}
INFO:tensorflow:Step 76200 per-step time 0.381s
I0123 15:29:36.830358 140287364614016 model_lib_v2.py:707] Step 76200 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018996641,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.43071,
 'Loss/RPNLoss/objectness_loss': 0.87012744,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 67.31983,
 'learning_rate': 0.02239315}
I0123 15:29:36.830646 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018996641,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 66.43071,
 'Loss/RPNLoss/objectness_loss': 0.87012744,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 67.31983,
 'learning_rate': 0.02239315}
INFO:tensorflow:Step 76300 per-step time 0.378s
I0123 15:30:14.626202 140287364614016 model_lib_v2.py:707] Step 76300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00031364983,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 58.800232,
 'Loss/RPNLoss/objectness_loss': 0.8350105,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 59.635555,
 'learning_rate': 0.022214599}
I0123 15:30:14.626487 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00031364983,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 58.800232,
 'Loss/RPNLoss/objectness_loss': 0.8350105,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 59.635555,
 'learning_rate': 0.022214599}
INFO:tensorflow:Step 76400 per-step time 0.382s
I0123 15:30:52.808140 140287364614016 model_lib_v2.py:707] Step 76400 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07520822,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 67.06554,
 'Loss/RPNLoss/objectness_loss': 0.8602175,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 68.00096,
 'learning_rate': 0.022036623}
I0123 15:30:52.808469 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07520822,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 67.06554,
 'Loss/RPNLoss/objectness_loss': 0.8602175,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 68.00096,
 'learning_rate': 0.022036623}
INFO:tensorflow:Step 76500 per-step time 0.383s
I0123 15:31:31.062405 140287364614016 model_lib_v2.py:707] Step 76500 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013287999,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 56.67209,
 'Loss/RPNLoss/objectness_loss': 1.6380124,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 58.311428,
 'learning_rate': 0.021859273}
I0123 15:31:31.062715 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013287999,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 56.67209,
 'Loss/RPNLoss/objectness_loss': 1.6380124,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 58.311428,
 'learning_rate': 0.021859273}
INFO:tensorflow:Step 76600 per-step time 0.376s
I0123 15:32:08.679230 140287364614016 model_lib_v2.py:707] Step 76600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.8231729,
 'Loss/BoxClassifierLoss/localization_loss': 1.0577002,
 'Loss/RPNLoss/localization_loss': 0.13789749,
 'Loss/RPNLoss/objectness_loss': 1.1101811,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.128952,
 'learning_rate': 0.02168251}
I0123 15:32:08.679522 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.8231729,
 'Loss/BoxClassifierLoss/localization_loss': 1.0577002,
 'Loss/RPNLoss/localization_loss': 0.13789749,
 'Loss/RPNLoss/objectness_loss': 1.1101811,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.128952,
 'learning_rate': 0.02168251}
INFO:tensorflow:Step 76700 per-step time 0.382s
I0123 15:32:46.856561 140287364614016 model_lib_v2.py:707] Step 76700 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0012362511,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 5.1113415,
 'Loss/RPNLoss/objectness_loss': 0.67847073,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7910485,
 'learning_rate': 0.021506352}
I0123 15:32:46.856862 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0012362511,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 5.1113415,
 'Loss/RPNLoss/objectness_loss': 0.67847073,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 5.7910485,
 'learning_rate': 0.021506352}
INFO:tensorflow:Step 76800 per-step time 0.377s
I0123 15:33:24.587571 140287364614016 model_lib_v2.py:707] Step 76800 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00025268336,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.7412455,
 'Loss/RPNLoss/objectness_loss': 1.0087774,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7502756,
 'learning_rate': 0.0213308}
I0123 15:33:24.587913 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00025268336,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.7412455,
 'Loss/RPNLoss/objectness_loss': 1.0087774,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.7502756,
 'learning_rate': 0.0213308}
INFO:tensorflow:Step 76900 per-step time 0.380s
I0123 15:34:02.624104 140287364614016 model_lib_v2.py:707] Step 76900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0030012187,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 13.331156,
 'Loss/RPNLoss/objectness_loss': 0.12604812,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.460205,
 'learning_rate': 0.021155871}
I0123 15:34:02.624394 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0030012187,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 13.331156,
 'Loss/RPNLoss/objectness_loss': 0.12604812,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.460205,
 'learning_rate': 0.021155871}
INFO:tensorflow:Step 77000 per-step time 0.379s
I0123 15:34:40.532523 140287364614016 model_lib_v2.py:707] Step 77000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0919218,
 'Loss/BoxClassifierLoss/localization_loss': 0.140659,
 'Loss/RPNLoss/localization_loss': 47.101585,
 'Loss/RPNLoss/objectness_loss': 0.22491439,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 48.559082,
 'learning_rate': 0.02098154}
I0123 15:34:40.532835 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0919218,
 'Loss/BoxClassifierLoss/localization_loss': 0.140659,
 'Loss/RPNLoss/localization_loss': 47.101585,
 'Loss/RPNLoss/objectness_loss': 0.22491439,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 48.559082,
 'learning_rate': 0.02098154}
INFO:tensorflow:Step 77100 per-step time 0.412s
I0123 15:35:21.712686 140287364614016 model_lib_v2.py:707] Step 77100 per-step time 0.412s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00040932227,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.0656824,
 'Loss/RPNLoss/objectness_loss': 0.81322575,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.8793175,
 'learning_rate': 0.020807832}
I0123 15:35:21.713036 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00040932227,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 3.0656824,
 'Loss/RPNLoss/objectness_loss': 0.81322575,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.8793175,
 'learning_rate': 0.020807832}
INFO:tensorflow:Step 77200 per-step time 0.380s
I0123 15:35:59.719931 140287364614016 model_lib_v2.py:707] Step 77200 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0003581087,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.5048707,
 'Loss/RPNLoss/objectness_loss': 1.4491222,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.954351,
 'learning_rate': 0.020634731}
I0123 15:35:59.720236 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0003581087,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.5048707,
 'Loss/RPNLoss/objectness_loss': 1.4491222,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.954351,
 'learning_rate': 0.020634731}
INFO:tensorflow:Step 77300 per-step time 0.380s
I0123 15:36:37.753147 140287364614016 model_lib_v2.py:707] Step 77300 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00035074557,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.5065867,
 'Loss/RPNLoss/objectness_loss': 2.1220837,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.6290212,
 'learning_rate': 0.020462245}
I0123 15:36:37.753439 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00035074557,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.5065867,
 'Loss/RPNLoss/objectness_loss': 2.1220837,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.6290212,
 'learning_rate': 0.020462245}
INFO:tensorflow:Step 77400 per-step time 0.377s
I0123 15:37:15.425751 140287364614016 model_lib_v2.py:707] Step 77400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.08561217,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.38058013,
 'Loss/RPNLoss/objectness_loss': 1.6345601,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1007524,
 'learning_rate': 0.020290375}
I0123 15:37:15.426166 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.08561217,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.38058013,
 'Loss/RPNLoss/objectness_loss': 1.6345601,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1007524,
 'learning_rate': 0.020290375}
INFO:tensorflow:Step 77500 per-step time 0.377s
I0123 15:37:53.174696 140287364614016 model_lib_v2.py:707] Step 77500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.25791025,
 'Loss/BoxClassifierLoss/localization_loss': 0.06800346,
 'Loss/RPNLoss/localization_loss': 48.177216,
 'Loss/RPNLoss/objectness_loss': 0.5806861,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 49.083817,
 'learning_rate': 0.020119138}
I0123 15:37:53.175018 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.25791025,
 'Loss/BoxClassifierLoss/localization_loss': 0.06800346,
 'Loss/RPNLoss/localization_loss': 48.177216,
 'Loss/RPNLoss/objectness_loss': 0.5806861,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 49.083817,
 'learning_rate': 0.020119138}
INFO:tensorflow:Step 77600 per-step time 0.380s
I0123 15:38:31.187711 140287364614016 model_lib_v2.py:707] Step 77600 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0043492774,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 70.53496,
 'Loss/RPNLoss/objectness_loss': 1.3098918,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 71.8492,
 'learning_rate': 0.01994851}
I0123 15:38:31.188009 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0043492774,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 70.53496,
 'Loss/RPNLoss/objectness_loss': 1.3098918,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 71.8492,
 'learning_rate': 0.01994851}
INFO:tensorflow:Step 77700 per-step time 0.381s
I0123 15:39:09.246784 140287364614016 model_lib_v2.py:707] Step 77700 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.003981524,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 21.39299,
 'Loss/RPNLoss/objectness_loss': 4.3864145,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 25.783386,
 'learning_rate': 0.019778503}
I0123 15:39:09.247107 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.003981524,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 21.39299,
 'Loss/RPNLoss/objectness_loss': 4.3864145,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 25.783386,
 'learning_rate': 0.019778503}
INFO:tensorflow:Step 77800 per-step time 0.380s
I0123 15:39:47.234484 140287364614016 model_lib_v2.py:707] Step 77800 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.023932254,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.945105,
 'Loss/RPNLoss/objectness_loss': 1.521908,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.4909453,
 'learning_rate': 0.01960914}
I0123 15:39:47.234776 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.023932254,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.945105,
 'Loss/RPNLoss/objectness_loss': 1.521908,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.4909453,
 'learning_rate': 0.01960914}
INFO:tensorflow:Step 77900 per-step time 0.380s
I0123 15:40:25.224005 140287364614016 model_lib_v2.py:707] Step 77900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0051976857,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 19.76358,
 'Loss/RPNLoss/objectness_loss': 0.28949046,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.058268,
 'learning_rate': 0.019440388}
I0123 15:40:25.224303 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0051976857,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 19.76358,
 'Loss/RPNLoss/objectness_loss': 0.28949046,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 20.058268,
 'learning_rate': 0.019440388}
INFO:tensorflow:Step 78000 per-step time 0.383s
I0123 15:41:03.494305 140287364614016 model_lib_v2.py:707] Step 78000 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10988955,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.9954338,
 'Loss/RPNLoss/objectness_loss': 1.735971,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.8412943,
 'learning_rate': 0.019272279}
I0123 15:41:03.494628 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10988955,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.9954338,
 'Loss/RPNLoss/objectness_loss': 1.735971,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 4.8412943,
 'learning_rate': 0.019272279}
INFO:tensorflow:Step 78100 per-step time 0.419s
I0123 15:41:45.384567 140287364614016 model_lib_v2.py:707] Step 78100 per-step time 0.419s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00046214278,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.931183,
 'Loss/RPNLoss/objectness_loss': 0.22064924,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 54.152294,
 'learning_rate': 0.019104786}
I0123 15:41:45.384884 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00046214278,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 53.931183,
 'Loss/RPNLoss/objectness_loss': 0.22064924,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 54.152294,
 'learning_rate': 0.019104786}
INFO:tensorflow:Step 78200 per-step time 0.382s
I0123 15:42:23.611041 140287364614016 model_lib_v2.py:707] Step 78200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0039063143,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 54.510292,
 'Loss/RPNLoss/objectness_loss': 2.6017735,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 57.11597,
 'learning_rate': 0.018937927}
I0123 15:42:23.611338 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0039063143,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 54.510292,
 'Loss/RPNLoss/objectness_loss': 2.6017735,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 57.11597,
 'learning_rate': 0.018937927}
INFO:tensorflow:Step 78300 per-step time 0.381s
I0123 15:43:01.742781 140287364614016 model_lib_v2.py:707] Step 78300 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.68273747,
 'Loss/BoxClassifierLoss/localization_loss': 0.5587101,
 'Loss/RPNLoss/localization_loss': 51.242477,
 'Loss/RPNLoss/objectness_loss': 0.60192883,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 53.085854,
 'learning_rate': 0.0187717}
I0123 15:43:01.743110 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.68273747,
 'Loss/BoxClassifierLoss/localization_loss': 0.5587101,
 'Loss/RPNLoss/localization_loss': 51.242477,
 'Loss/RPNLoss/objectness_loss': 0.60192883,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 53.085854,
 'learning_rate': 0.0187717}
INFO:tensorflow:Step 78400 per-step time 0.378s
I0123 15:43:39.522013 140287364614016 model_lib_v2.py:707] Step 78400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5489243,
 'Loss/BoxClassifierLoss/localization_loss': 0.9277016,
 'Loss/RPNLoss/localization_loss': 10.535217,
 'Loss/RPNLoss/objectness_loss': 0.45664868,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.468492,
 'learning_rate': 0.018606123}
I0123 15:43:39.522307 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5489243,
 'Loss/BoxClassifierLoss/localization_loss': 0.9277016,
 'Loss/RPNLoss/localization_loss': 10.535217,
 'Loss/RPNLoss/objectness_loss': 0.45664868,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 13.468492,
 'learning_rate': 0.018606123}
INFO:tensorflow:Step 78500 per-step time 0.378s
I0123 15:44:17.358415 140287364614016 model_lib_v2.py:707] Step 78500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008372214,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 92.36749,
 'Loss/RPNLoss/objectness_loss': 2.988128,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.36399,
 'learning_rate': 0.01844117}
I0123 15:44:17.358734 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008372214,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 92.36749,
 'Loss/RPNLoss/objectness_loss': 2.988128,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 95.36399,
 'learning_rate': 0.01844117}
INFO:tensorflow:Step 78600 per-step time 0.382s
I0123 15:44:55.526202 140287364614016 model_lib_v2.py:707] Step 78600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1489487,
 'Loss/BoxClassifierLoss/localization_loss': 0.42087767,
 'Loss/RPNLoss/localization_loss': 36.483093,
 'Loss/RPNLoss/objectness_loss': 0.48677328,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 38.539692,
 'learning_rate': 0.018276868}
I0123 15:44:55.526542 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1489487,
 'Loss/BoxClassifierLoss/localization_loss': 0.42087767,
 'Loss/RPNLoss/localization_loss': 36.483093,
 'Loss/RPNLoss/objectness_loss': 0.48677328,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 38.539692,
 'learning_rate': 0.018276868}
INFO:tensorflow:Step 78700 per-step time 0.380s
I0123 15:45:33.569119 140287364614016 model_lib_v2.py:707] Step 78700 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.50374544,
 'Loss/BoxClassifierLoss/localization_loss': 0.2797015,
 'Loss/RPNLoss/localization_loss': 36.63706,
 'Loss/RPNLoss/objectness_loss': 0.5933877,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 38.013893,
 'learning_rate': 0.018113194}
I0123 15:45:33.569411 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.50374544,
 'Loss/BoxClassifierLoss/localization_loss': 0.2797015,
 'Loss/RPNLoss/localization_loss': 36.63706,
 'Loss/RPNLoss/objectness_loss': 0.5933877,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 38.013893,
 'learning_rate': 0.018113194}
INFO:tensorflow:Step 78800 per-step time 0.376s
I0123 15:46:11.132283 140287364614016 model_lib_v2.py:707] Step 78800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.05237865,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 55.568527,
 'Loss/RPNLoss/objectness_loss': 0.33276504,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 55.95367,
 'learning_rate': 0.017950162}
I0123 15:46:11.132573 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.05237865,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 55.568527,
 'Loss/RPNLoss/objectness_loss': 0.33276504,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 55.95367,
 'learning_rate': 0.017950162}
INFO:tensorflow:Step 78900 per-step time 0.381s
I0123 15:46:49.271357 140287364614016 model_lib_v2.py:707] Step 78900 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.025114423,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.16968846,
 'Loss/RPNLoss/objectness_loss': 0.52933884,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.7241417,
 'learning_rate': 0.017787775}
I0123 15:46:49.271667 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.025114423,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.16968846,
 'Loss/RPNLoss/objectness_loss': 0.52933884,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.7241417,
 'learning_rate': 0.017787775}
INFO:tensorflow:Step 79000 per-step time 0.379s
I0123 15:47:27.190949 140287364614016 model_lib_v2.py:707] Step 79000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0853711,
 'Loss/BoxClassifierLoss/localization_loss': 0.66082025,
 'Loss/RPNLoss/localization_loss': 0.3477788,
 'Loss/RPNLoss/objectness_loss': 1.6710162,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.7649865,
 'learning_rate': 0.017626047}
I0123 15:47:27.191260 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0853711,
 'Loss/BoxClassifierLoss/localization_loss': 0.66082025,
 'Loss/RPNLoss/localization_loss': 0.3477788,
 'Loss/RPNLoss/objectness_loss': 1.6710162,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.7649865,
 'learning_rate': 0.017626047}
INFO:tensorflow:Step 79100 per-step time 0.424s
I0123 15:48:09.570188 140287364614016 model_lib_v2.py:707] Step 79100 per-step time 0.424s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.2475022,
 'Loss/BoxClassifierLoss/localization_loss': 1.8867043,
 'Loss/RPNLoss/localization_loss': 71.772606,
 'Loss/RPNLoss/objectness_loss': 1.4518687,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 76.35868,
 'learning_rate': 0.017464956}
I0123 15:48:09.570483 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.2475022,
 'Loss/BoxClassifierLoss/localization_loss': 1.8867043,
 'Loss/RPNLoss/localization_loss': 71.772606,
 'Loss/RPNLoss/objectness_loss': 1.4518687,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 76.35868,
 'learning_rate': 0.017464956}
INFO:tensorflow:Step 79200 per-step time 0.381s
I0123 15:48:47.700515 140287364614016 model_lib_v2.py:707] Step 79200 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6267061,
 'Loss/BoxClassifierLoss/localization_loss': 1.695502,
 'Loss/RPNLoss/localization_loss': 1.3945422,
 'Loss/RPNLoss/objectness_loss': 1.3122969,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.0290475,
 'learning_rate': 0.017304525}
I0123 15:48:47.700846 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6267061,
 'Loss/BoxClassifierLoss/localization_loss': 1.695502,
 'Loss/RPNLoss/localization_loss': 1.3945422,
 'Loss/RPNLoss/objectness_loss': 1.3122969,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 6.0290475,
 'learning_rate': 0.017304525}
INFO:tensorflow:Step 79300 per-step time 0.382s
I0123 15:49:25.945189 140287364614016 model_lib_v2.py:707] Step 79300 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.6801853,
 'Loss/BoxClassifierLoss/localization_loss': 0.27682108,
 'Loss/RPNLoss/localization_loss': 0.053858586,
 'Loss/RPNLoss/objectness_loss': 0.68688375,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.6977488,
 'learning_rate': 0.017144732}
I0123 15:49:25.945497 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.6801853,
 'Loss/BoxClassifierLoss/localization_loss': 0.27682108,
 'Loss/RPNLoss/localization_loss': 0.053858586,
 'Loss/RPNLoss/objectness_loss': 0.68688375,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.6977488,
 'learning_rate': 0.017144732}
INFO:tensorflow:Step 79400 per-step time 0.381s
I0123 15:50:04.008481 140287364614016 model_lib_v2.py:707] Step 79400 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0008747508,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.3517666,
 'Loss/RPNLoss/objectness_loss': 0.59248525,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.9451265,
 'learning_rate': 0.016985593}
I0123 15:50:04.008798 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0008747508,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 2.3517666,
 'Loss/RPNLoss/objectness_loss': 0.59248525,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.9451265,
 'learning_rate': 0.016985593}
INFO:tensorflow:Step 79500 per-step time 0.379s
I0123 15:50:41.932635 140287364614016 model_lib_v2.py:707] Step 79500 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00048257742,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.84862,
 'Loss/RPNLoss/objectness_loss': 0.1088061,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.9579087,
 'learning_rate': 0.01682712}
I0123 15:50:41.932975 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00048257742,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.84862,
 'Loss/RPNLoss/objectness_loss': 0.1088061,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.9579087,
 'learning_rate': 0.01682712}
INFO:tensorflow:Step 79600 per-step time 0.376s
I0123 15:51:19.498036 140287364614016 model_lib_v2.py:707] Step 79600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.39011973,
 'Loss/BoxClassifierLoss/localization_loss': 0.12240758,
 'Loss/RPNLoss/localization_loss': 1.0416999,
 'Loss/RPNLoss/objectness_loss': 0.62105924,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1752863,
 'learning_rate': 0.016669286}
I0123 15:51:19.498351 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.39011973,
 'Loss/BoxClassifierLoss/localization_loss': 0.12240758,
 'Loss/RPNLoss/localization_loss': 1.0416999,
 'Loss/RPNLoss/objectness_loss': 0.62105924,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.1752863,
 'learning_rate': 0.016669286}
INFO:tensorflow:Step 79700 per-step time 0.382s
I0123 15:51:57.683816 140287364614016 model_lib_v2.py:707] Step 79700 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.18671319,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.6334281,
 'Loss/RPNLoss/objectness_loss': 0.69709295,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.5172342,
 'learning_rate': 0.016512126}
I0123 15:51:57.684128 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.18671319,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.6334281,
 'Loss/RPNLoss/objectness_loss': 0.69709295,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 1.5172342,
 'learning_rate': 0.016512126}
INFO:tensorflow:Step 79800 per-step time 0.386s
I0123 15:52:36.293656 140287364614016 model_lib_v2.py:707] Step 79800 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0334463,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 54.779125,
 'Loss/RPNLoss/objectness_loss': 1.5619979,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 56.37457,
 'learning_rate': 0.016355604}
I0123 15:52:36.293973 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0334463,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 54.779125,
 'Loss/RPNLoss/objectness_loss': 1.5619979,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 56.37457,
 'learning_rate': 0.016355604}
INFO:tensorflow:Step 79900 per-step time 0.384s
I0123 15:53:14.669533 140287364614016 model_lib_v2.py:707] Step 79900 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.009759242,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.9375439,
 'Loss/RPNLoss/objectness_loss': 1.6338415,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.5811446,
 'learning_rate': 0.01619976}
I0123 15:53:14.669855 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.009759242,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 1.9375439,
 'Loss/RPNLoss/objectness_loss': 1.6338415,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.5811446,
 'learning_rate': 0.01619976}
INFO:tensorflow:Step 80000 per-step time 0.381s
I0123 15:53:52.814599 140287364614016 model_lib_v2.py:707] Step 80000 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1444095,
 'Loss/BoxClassifierLoss/localization_loss': 0.88752365,
 'Loss/RPNLoss/localization_loss': 82.020805,
 'Loss/RPNLoss/objectness_loss': 1.8583052,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.91104,
 'learning_rate': 0.016044574}
I0123 15:53:52.814925 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1444095,
 'Loss/BoxClassifierLoss/localization_loss': 0.88752365,
 'Loss/RPNLoss/localization_loss': 82.020805,
 'Loss/RPNLoss/objectness_loss': 1.8583052,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 85.91104,
 'learning_rate': 0.016044574}
INFO:tensorflow:Step 80100 per-step time 0.411s
I0123 15:54:33.915232 140287364614016 model_lib_v2.py:707] Step 80100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0009408,
 'Loss/BoxClassifierLoss/localization_loss': 0.523782,
 'Loss/RPNLoss/localization_loss': 0.31254238,
 'Loss/RPNLoss/objectness_loss': 0.5868543,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.4241195,
 'learning_rate': 0.015890054}
I0123 15:54:33.915529 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0009408,
 'Loss/BoxClassifierLoss/localization_loss': 0.523782,
 'Loss/RPNLoss/localization_loss': 0.31254238,
 'Loss/RPNLoss/objectness_loss': 0.5868543,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 2.4241195,
 'learning_rate': 0.015890054}
INFO:tensorflow:Step 80200 per-step time 0.385s
I0123 15:55:12.369825 140287364614016 model_lib_v2.py:707] Step 80200 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.8549322,
 'Loss/BoxClassifierLoss/localization_loss': 1.5488619,
 'Loss/RPNLoss/localization_loss': 26.58376,
 'Loss/RPNLoss/objectness_loss': 0.4488349,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 29.436388,
 'learning_rate': 0.015736198}
I0123 15:55:12.370136 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.8549322,
 'Loss/BoxClassifierLoss/localization_loss': 1.5488619,
 'Loss/RPNLoss/localization_loss': 26.58376,
 'Loss/RPNLoss/objectness_loss': 0.4488349,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 29.436388,
 'learning_rate': 0.015736198}
INFO:tensorflow:Step 80300 per-step time 0.383s
I0123 15:55:50.654390 140287364614016 model_lib_v2.py:707] Step 80300 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1576238,
 'Loss/BoxClassifierLoss/localization_loss': 0.6040846,
 'Loss/RPNLoss/localization_loss': 63.860676,
 'Loss/RPNLoss/objectness_loss': 1.0205679,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 66.64295,
 'learning_rate': 0.015583005}
I0123 15:55:50.654678 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1576238,
 'Loss/BoxClassifierLoss/localization_loss': 0.6040846,
 'Loss/RPNLoss/localization_loss': 63.860676,
 'Loss/RPNLoss/objectness_loss': 1.0205679,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 66.64295,
 'learning_rate': 0.015583005}
INFO:tensorflow:Step 80400 per-step time 0.378s
I0123 15:56:28.493222 140287364614016 model_lib_v2.py:707] Step 80400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5179384,
 'Loss/BoxClassifierLoss/localization_loss': 1.102843,
 'Loss/RPNLoss/localization_loss': 0.39166912,
 'Loss/RPNLoss/objectness_loss': 0.27415758,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.286608,
 'learning_rate': 0.015430493}
I0123 15:56:28.493552 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5179384,
 'Loss/BoxClassifierLoss/localization_loss': 1.102843,
 'Loss/RPNLoss/localization_loss': 0.39166912,
 'Loss/RPNLoss/objectness_loss': 0.27415758,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 3.286608,
 'learning_rate': 0.015430493}
INFO:tensorflow:Step 80500 per-step time 0.386s
I0123 15:57:07.122789 140287364614016 model_lib_v2.py:707] Step 80500 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.423513,
 'Loss/BoxClassifierLoss/localization_loss': 0.6655255,
 'Loss/RPNLoss/localization_loss': 53.97462,
 'Loss/RPNLoss/objectness_loss': 0.85856605,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 56.922226,
 'learning_rate': 0.015278635}
I0123 15:57:07.123116 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.423513,
 'Loss/BoxClassifierLoss/localization_loss': 0.6655255,
 'Loss/RPNLoss/localization_loss': 53.97462,
 'Loss/RPNLoss/objectness_loss': 0.85856605,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 56.922226,
 'learning_rate': 0.015278635}
INFO:tensorflow:Step 80600 per-step time 0.382s
I0123 15:57:45.293949 140287364614016 model_lib_v2.py:707] Step 80600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.7237444,
 'Loss/BoxClassifierLoss/localization_loss': 0.8998036,
 'Loss/RPNLoss/localization_loss': 29.695911,
 'Loss/RPNLoss/objectness_loss': 0.5629089,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.88237,
 'learning_rate': 0.015127453}
I0123 15:57:45.294242 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.7237444,
 'Loss/BoxClassifierLoss/localization_loss': 0.8998036,
 'Loss/RPNLoss/localization_loss': 29.695911,
 'Loss/RPNLoss/objectness_loss': 0.5629089,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.88237,
 'learning_rate': 0.015127453}
INFO:tensorflow:Step 80700 per-step time 0.383s
I0123 15:58:23.558974 140287364614016 model_lib_v2.py:707] Step 80700 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001082171,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 52.320652,
 'Loss/RPNLoss/objectness_loss': 0.43119323,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 52.752926,
 'learning_rate': 0.014976959}
I0123 15:58:23.559369 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001082171,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 52.320652,
 'Loss/RPNLoss/objectness_loss': 0.43119323,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 52.752926,
 'learning_rate': 0.014976959}
INFO:tensorflow:Step 80800 per-step time 0.386s
I0123 15:59:02.129001 140287364614016 model_lib_v2.py:707] Step 80800 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.019703379,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 31.327469,
 'Loss/RPNLoss/objectness_loss': 1.307095,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.654266,
 'learning_rate': 0.014827128}
I0123 15:59:02.129316 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.019703379,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 31.327469,
 'Loss/RPNLoss/objectness_loss': 1.307095,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 32.654266,
 'learning_rate': 0.014827128}
INFO:tensorflow:Step 80900 per-step time 0.396s
I0123 15:59:41.755171 140287364614016 model_lib_v2.py:707] Step 80900 per-step time 0.396s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5337414,
 'Loss/BoxClassifierLoss/localization_loss': 0.1393968,
 'Loss/RPNLoss/localization_loss': 24.967594,
 'Loss/RPNLoss/objectness_loss': 0.33450016,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 25.975233,
 'learning_rate': 0.014677968}
I0123 15:59:41.755471 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5337414,
 'Loss/BoxClassifierLoss/localization_loss': 0.1393968,
 'Loss/RPNLoss/localization_loss': 24.967594,
 'Loss/RPNLoss/objectness_loss': 0.33450016,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 25.975233,
 'learning_rate': 0.014677968}
INFO:tensorflow:Step 81000 per-step time 0.385s
I0123 16:00:20.294591 140287364614016 model_lib_v2.py:707] Step 81000 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00024240505,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 57.503975,
 'Loss/RPNLoss/objectness_loss': 0.20698807,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 57.711205,
 'learning_rate': 0.014529499}
I0123 16:00:20.294896 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00024240505,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 57.503975,
 'Loss/RPNLoss/objectness_loss': 0.20698807,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 57.711205,
 'learning_rate': 0.014529499}
INFO:tensorflow:Step 81100 per-step time 0.418s
I0123 16:01:02.056276 140287364614016 model_lib_v2.py:707] Step 81100 per-step time 0.418s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11626255,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.1249623,
 'Loss/RPNLoss/objectness_loss': 2.226023,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.467248,
 'learning_rate': 0.014381699}
I0123 16:01:02.056622 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11626255,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 6.1249623,
 'Loss/RPNLoss/objectness_loss': 2.226023,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 8.467248,
 'learning_rate': 0.014381699}
INFO:tensorflow:Step 81200 per-step time 0.385s
I0123 16:01:40.548032 140287364614016 model_lib_v2.py:707] Step 81200 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008738259,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 36.51449,
 'Loss/RPNLoss/objectness_loss': 0.5205713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 37.043797,
 'learning_rate': 0.014234595}
I0123 16:01:40.548349 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008738259,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 36.51449,
 'Loss/RPNLoss/objectness_loss': 0.5205713,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 37.043797,
 'learning_rate': 0.014234595}
INFO:tensorflow:Step 81300 per-step time 0.385s
I0123 16:02:19.061013 140287364614016 model_lib_v2.py:707] Step 81300 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00025892313,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.42648917,
 'Loss/RPNLoss/objectness_loss': 0.54572624,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.97247434,
 'learning_rate': 0.014088149}
I0123 16:02:19.061323 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00025892313,
 'Loss/BoxClassifierLoss/localization_loss': 0.0,
 'Loss/RPNLoss/localization_loss': 0.42648917,
 'Loss/RPNLoss/objectness_loss': 0.54572624,
 'Loss/regularization_loss': 0.0,
 'Loss/total_loss': 0.97247434,
 'learning_rate': 0.014088149}
^C


!python model_main_tf2.py --model_dir=models/my_rcnn_imagenet --pipeline_config_path=models/my_rcnn_imagenet/pipeline.config --checkpoint_dir=models/my_rcnn_imagenet

WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
W0124 14:30:30.150501 140299072845696 model_lib_v2.py:1090] Forced number of epochs for all eval validations to be 1.
INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None
I0124 14:30:30.150705 140299072845696 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0124 14:30:30.150771 140299072845696 config_util.py:552] Maybe overwriting use_bfloat16: False
INFO:tensorflow:Maybe overwriting eval_num_epochs: 1
I0124 14:30:30.150831 140299072845696 config_util.py:552] Maybe overwriting eval_num_epochs: 1
WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
W0124 14:30:30.150916 140299072845696 model_lib_v2.py:1111] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
2022-01-24 14:30:30.894094: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Reading unweighted datasets: ['./annotations/test.record']
I0124 14:30:30.957651 140299072845696 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/test.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/test.record']
I0124 14:30:30.957953 140299072845696 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/test.record']
INFO:tensorflow:Number of filenames to read: 1
I0124 14:30:30.958027 140299072845696 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0124 14:30:30.958081 140299072845696 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0124 14:30:30.960322 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0124 14:30:30.980329 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0124 14:30:35.073913 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0124 14:30:36.242375 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Waiting for new checkpoint at models/my_rcnn_imagenet
I0124 14:30:38.818378 140299072845696 checkpoint_utils.py:140] Waiting for new checkpoint at models/my_rcnn_imagenet
INFO:tensorflow:Found new checkpoint at models/my_rcnn_imagenet/ckpt-82
I0124 14:30:38.821352 140299072845696 checkpoint_utils.py:149] Found new checkpoint at models/my_rcnn_imagenet/ckpt-82
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:depth of additional conv before box predictor: 0
I0124 14:30:53.359992 140299072845696 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0124 14:31:00.592570 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See `tf.nn.softmax_cross_entropy_with_logits_v2`.

W0124 14:31:06.957759 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See `tf.nn.softmax_cross_entropy_with_logits_v2`.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0124 14:32:15.174481 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Finished eval step 0
I0124 14:32:15.183495 140299072845696 model_lib_v2.py:966] Finished eval step 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
W0124 14:32:15.350790 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
INFO:tensorflow:Finished eval step 100
I0124 14:33:26.618737 140299072845696 model_lib_v2.py:966] Finished eval step 100
INFO:tensorflow:Finished eval step 200
I0124 14:34:34.286096 140299072845696 model_lib_v2.py:966] Finished eval step 200
INFO:tensorflow:Finished eval step 300
I0124 14:35:42.027490 140299072845696 model_lib_v2.py:966] Finished eval step 300
INFO:tensorflow:Finished eval step 400
I0124 14:36:49.769926 140299072845696 model_lib_v2.py:966] Finished eval step 400
INFO:tensorflow:Finished eval step 500
I0124 14:37:57.465292 140299072845696 model_lib_v2.py:966] Finished eval step 500
INFO:tensorflow:Finished eval step 600
I0124 14:39:05.096886 140299072845696 model_lib_v2.py:966] Finished eval step 600
INFO:tensorflow:Finished eval step 700
I0124 14:40:13.097734 140299072845696 model_lib_v2.py:966] Finished eval step 700
INFO:tensorflow:Finished eval step 800
I0124 14:41:20.774344 140299072845696 model_lib_v2.py:966] Finished eval step 800
INFO:tensorflow:Finished eval step 900
I0124 14:42:28.617234 140299072845696 model_lib_v2.py:966] Finished eval step 900
INFO:tensorflow:Finished eval step 1000
I0124 14:43:36.402159 140299072845696 model_lib_v2.py:966] Finished eval step 1000
INFO:tensorflow:Finished eval step 1100
I0124 14:44:43.999557 140299072845696 model_lib_v2.py:966] Finished eval step 1100
INFO:tensorflow:Finished eval step 1200
I0124 14:45:51.635493 140299072845696 model_lib_v2.py:966] Finished eval step 1200
INFO:tensorflow:Finished eval step 1300
I0124 14:46:59.413781 140299072845696 model_lib_v2.py:966] Finished eval step 1300
INFO:tensorflow:Finished eval step 1400
I0124 14:48:07.252988 140299072845696 model_lib_v2.py:966] Finished eval step 1400
INFO:tensorflow:Finished eval step 1500
I0124 14:49:14.680690 140299072845696 model_lib_v2.py:966] Finished eval step 1500
INFO:tensorflow:Finished eval step 1600
I0124 14:50:22.385610 140299072845696 model_lib_v2.py:966] Finished eval step 1600
INFO:tensorflow:Performing evaluation on 1629 images.
I0124 14:50:41.293404 140299072845696 coco_evaluation.py:293] Performing evaluation on 1629 images.
creating index...
index created!
INFO:tensorflow:Loading and preparing annotation results...
I0124 14:50:41.296270 140299072845696 coco_tools.py:116] Loading and preparing annotation results...
INFO:tensorflow:DONE (t=0.00s)
I0124 14:50:41.297290 140299072845696 coco_tools.py:138] DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=3.93s).
Accumulating evaluation results...
DONE (t=0.91s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:tensorflow:Eval metrics at step 81000
I0124 14:50:46.247649 140299072845696 model_lib_v2.py:1015] Eval metrics at step 81000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP: 0.000000
I0124 14:50:46.262341 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP@.50IOU: 0.000000
I0124 14:50:46.263627 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP@.50IOU: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP@.75IOU: 0.000000
I0124 14:50:46.264754 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP@.75IOU: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP (small): -1.000000
I0124 14:50:46.265940 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP (small): -1.000000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP (medium): 0.000000
I0124 14:50:46.266924 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP (medium): 0.000000
INFO:tensorflow:	+ DetectionBoxes_Precision/mAP (large): 0.000000
I0124 14:50:46.267962 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Precision/mAP (large): 0.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@1: 0.000000
I0124 14:50:46.269064 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@1: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@10: 0.000000
I0124 14:50:46.270041 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@10: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@100: 0.000000
I0124 14:50:46.271005 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@100: 0.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@100 (small): -1.000000
I0124 14:50:46.271989 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@100 (small): -1.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@100 (medium): 0.000000
I0124 14:50:46.273025 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@100 (medium): 0.000000
INFO:tensorflow:	+ DetectionBoxes_Recall/AR@100 (large): 0.000000
I0124 14:50:46.274218 140299072845696 model_lib_v2.py:1018] 	+ DetectionBoxes_Recall/AR@100 (large): 0.000000
INFO:tensorflow:	+ Loss/RPNLoss/localization_loss: 39.191086
I0124 14:50:46.275136 140299072845696 model_lib_v2.py:1018] 	+ Loss/RPNLoss/localization_loss: 39.191086
INFO:tensorflow:	+ Loss/RPNLoss/objectness_loss: 0.310603
I0124 14:50:46.276059 140299072845696 model_lib_v2.py:1018] 	+ Loss/RPNLoss/objectness_loss: 0.310603
INFO:tensorflow:	+ Loss/BoxClassifierLoss/localization_loss: 0.000000
I0124 14:50:46.276993 140299072845696 model_lib_v2.py:1018] 	+ Loss/BoxClassifierLoss/localization_loss: 0.000000
INFO:tensorflow:	+ Loss/BoxClassifierLoss/classification_loss: 0.000000
I0124 14:50:46.277999 140299072845696 model_lib_v2.py:1018] 	+ Loss/BoxClassifierLoss/classification_loss: 0.000000
INFO:tensorflow:	+ Loss/regularization_loss: 0.000000
I0124 14:50:46.279058 140299072845696 model_lib_v2.py:1018] 	+ Loss/regularization_loss: 0.000000
INFO:tensorflow:	+ Loss/total_loss: 39.501694
I0124 14:50:46.280007 140299072845696 model_lib_v2.py:1018] 	+ Loss/total_loss: 39.501694
INFO:tensorflow:Waiting for new checkpoint at models/my_rcnn_imagenet
I0124 14:50:47.107184 140299072845696 checkpoint_utils.py:140] Waiting for new checkpoint at models/my_rcnn_imagenet


!python ./exporter_main_v2.py --input_type image_tensor --pipeline_config_path ./models/my_rcnn_imagenet/pipeline.config --trained_checkpoint_dir ./models/my_rcnn_imagenet/ --output_directory ./exported-models/my_model_rcnn

2022-01-24 15:34:34.650881: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
W0124 15:34:36.505764 140620629907328 deprecation.py:619] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
INFO:tensorflow:depth of additional conv before box predictor: 0
I0124 15:34:52.334997 140620629907328 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0124 15:35:01.644590 140620629907328 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe41171d3d0>, because it is not built.
W0124 15:36:15.178672 140620629907328 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe41171d3d0>, because it is not built.
2022-01-24 15:36:46.982829: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
W0124 15:38:08.656437 140620629907328 save.py:268] Found untraced functions such as FirstStageBoxPredictor_layer_call_fn, FirstStageBoxPredictor_layer_call_and_return_conditional_losses, mask_rcnn_keras_box_predictor_layer_call_fn, mask_rcnn_keras_box_predictor_layer_call_and_return_conditional_losses, FirstStageBoxPredictor_layer_call_fn while saving (showing 5 of 70). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: ./exported-models/my_model_rcnn/saved_model/assets
I0124 15:38:31.600029 140620629907328 builder_impl.py:784] Assets written to: ./exported-models/my_model_rcnn/saved_model/assets
INFO:tensorflow:Writing pipeline config file to ./exported-models/my_model_rcnn/pipeline.config
I0124 15:38:33.973511 140620629907328 config_util.py:254] Writing pipeline config file to ./exported-models/my_model_rcnn/pipeline.config


yolo_path = os.path.join(capstone_path,"tensorflow/workspace/training-yolo")
os.chdir(yolo_path)


%%capture
!git clone https://github.com/ultralytics/yolov5.git
%cd /yolov5
!pip install -r requirements.txt


%cd ..

/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo


%cd yolov5

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5


cd training-yolo

[Errno 2] No such file or directory: 'training-yolo'
/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo

ls

 car_data_yolo/            preprocess-yolo.py   val_csv.csv
'Car names and make.csv'   train_csv.csv        yolov5/


os.chdir(capstone_path)

!ls

 Annotations		  Dataset		     resnet_final_weights.h5
 arun_notebook		  df_tr.csv		     resnet_weights.hdf5
 aug_images_resize	  df_ts.csv		     tensorflow
 augmented_images.csv	  labels.csv		     test.csv
'Car Images'		  losses.png		     tmp
 compressed_images	  model_plot.png	     train.csv
 compressed_test_images   resnet152_weights_tf.h5    train_csv.csv
 compressed_val_images	  resnet_best_weights.hdf5   val_csv.csv
 dataframes		  resnet_final.h5	    'yolo best'


#!cp -r compressed_images yolo-path


#!cp -r compressed_val_images yolo-path


#!cp -r compressed_test_images yolo-path


dp = '/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo'
dp_train = os.path.join(dp,'compressed_images')
dp_test = os.path.join(dp,'compressed_test_images')
dp_val = os.path.join(dp,'compressed_val_images')
os.chdir(dp)


out_dp = '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo'


df_train


df_val


df_test_ann


df_cars_name = pd.read_csv(os.path.join(dp,'Car names and make.csv'),names=['Car Name'])
df_cars_name

!ls

 car_data_yolo		   preprocess-yolo.py   val_csv.csv
'Car names and make.csv'   train_csv.csv        yolov5


count = 0
for root, dirs, files in os.walk(dp_train):
    print(root,dirs,files)
    for file in files:
        #print(file)
        count = count + 1
        path_file = os.path.join(root,file)
        shutil.copy2(path_file,os.path.join(out_dp + '/train/images'))

print(count)

0


for root, dirs, files in os.walk(dp_test):
    for file in files:
        path_file = os.path.join(root,file)
        shutil.copy2(path_file,out_dp + '/test/images')


count = 0
for root, dirs, files in os.walk(dp_val):
    print(root,dirs,files)
    for file in files:
        count = count + 1
        path_file = os.path.join(root,file)
        shutil.copy2(path_file,os.path.join(out_dp,'val/images'))

print(count)

0


df_test = df_test_ann.copy()


df_train['filepath'] = df_train['filename'].apply(lambda x:os.path.join(out_dp + '/train/images',x))
df_test['filepath'] = df_test['filename'].apply(lambda x:os.path.join(out_dp + '/test/images',x))
df_val['filepath'] = df_val['filename'].apply(lambda x:os.path.join(out_dp + '/val/images',x))


l = list(df_train['filepath'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images/06890.jpg


l = list(df_test['filepath'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images/00001.jpg


l = list(df_val['filepath'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images/04506.jpg


df_train['Label_Path'] = df_train['filename'].apply(lambda x:os.path.join(out_dp + '/train/labels',x.replace("jpg", "txt")))
df_test['Label_Path'] = df_test['filename'].apply(lambda x:os.path.join(out_dp + '/test/labels',x.replace("jpg", "txt")))
df_val['Label_Path'] = df_val['filename'].apply(lambda x:os.path.join(out_dp + '/val/labels',x.replace("jpg", "txt")))


l = list(df_val['Label_Path'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/labels/04506.txt


l = list(df_test['Label_Path'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/labels/00001.txt


l = list(df_train['Label_Path'])
print(l[0])

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06890.txt


for index, row in df_train.iterrows():
    x, y, w, h = YOLO_xywh(row['xmin'],row['ymin'],row['xmax'],row['ymax'],row['width'], row['height'])
    image_class_index = int(row['class']) - 1
    print(row['Label_Path'])
    with open(row['Label_Path'], "w") as text_file:
        print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06890.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03838.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03741.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05159.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03349.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03434.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02692.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02499.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07748.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02472.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00432.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02995.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01152.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07215.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03701.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07006.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01421.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01781.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04282.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06881.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02944.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03646.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00282.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03692.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05733.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04385.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04614.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06260.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07158.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03211.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03466.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05629.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05598.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04011.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02895.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05585.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00575.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05199.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06462.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00973.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06827.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03602.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04177.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07555.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06587.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02288.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06457.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02539.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07911.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05659.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01439.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00408.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07175.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02073.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05756.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05732.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07478.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00574.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01505.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05395.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03873.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02565.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07846.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05137.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01552.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07151.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04460.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00210.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/08072.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03979.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07321.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01074.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04601.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07965.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03488.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07997.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00158.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07866.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00703.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04917.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02924.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/08109.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07282.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07085.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/08133.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02353.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02831.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00352.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03653.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03788.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03499.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04243.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01537.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03242.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/08071.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03005.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01577.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03736.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02891.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01158.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07379.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05192.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05169.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00163.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02370.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02654.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06067.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05613.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06193.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05445.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04952.txt
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/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02126.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06608.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04739.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01622.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06764.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07844.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05666.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07138.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00648.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00060.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00935.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03509.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01961.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04575.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07131.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00588.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05652.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07255.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01940.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03737.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00767.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05153.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03573.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06271.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00857.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02792.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05273.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07561.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06362.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06335.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05341.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07080.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07470.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00077.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05409.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02015.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03956.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07290.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03853.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06508.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02246.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06283.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07140.txt
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03329.txt


for index, row in df_val.iterrows():
    x, y, w, h = YOLO_xywh(row['xmin'],	row['ymin'],row['xmax'],row['ymax'],row['width'], row['height'])
    image_class_index = int(row['class']) - 1
    print(row['Label_Path'])
    with open(row['Label_Path'], "w") as text_file:
        print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)


for index, row in df_test.iterrows():
    x, y, w, h = YOLO_xywh(row['xmin'],
                           row['ymin'],
                           row['xmax'],
                           row['ymax'],
                           row['width'],
                           row['height'])
    image_class_index = int(row['class']) - 1
    print(row['Label_Path'])
    with open(row['Label_Path'], "w") as text_file:
        print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)


%cd yolov5

/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo/yolov5


%cd data

/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo/yolov5/data


!touch carData.yaml


%%writefile carData.yaml

train: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images
val: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images
test: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images

# number of classes
nc: 196

# class names
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C/V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']


%cat carData.yaml

train: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images
val: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images
test: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images

# number of classes
nc: 196

# class names
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C/V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']


%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5


!python train.py --img 224 --cfg yolov5s.yaml --hyp hyp.scratch.yaml --batch 32 --epochs 200 --data carData.yaml --weights yolov5s.pt --workers 16 --name yolo_car_classification

Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
train: weights=yolov5s.pt, cfg=yolov5s.yaml, data=carData.yaml, hyp=hyp.scratch.yaml, epochs=200, batch_size=32, imgsz=224, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=16, project=runs/train, name=yolo_car_classification, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: Command 'git fetch && git config --get remote.origin.url' timed out after 5 seconds
YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)

hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
remote: Enumerating objects: 311, done.
remote: Counting objects: 100% (130/130), done.
remote: Total 311 (delta 130), reused 130 (delta 130), pack-reused 181
Receiving objects: 100% (311/311), 71.26 KiB | 51.00 KiB/s, done.
Resolving deltas: 100% (201/201), completed with 43 local objects.
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 270 layers, 7548241 parameters, 7548241 gradients

Transferred 342/349 items from yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
albumentations: version 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed
From https://github.com/ultralytics/yolov5
   16563ac..7539cd7  master             -> origin/master
   4c9e629..46a2a70  classifier         -> origin/classifier
   241cd84..487a014  tests/aws          -> origin/tests/aws
 * [new branch]      updates/benchmarks -> origin/updates/benchmarks
train: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels.cache' images and labels... 6515 found, 0 missing, 1 empty, 1 corrupt: 100% 6515/6515 [00:00<?, ?it/s]
train: WARNING: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images/07389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [     1.1476]
val: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/labels.cache' images and labels... 1629 found, 0 missing, 0 empty, 0 corrupt: 100% 1629/1629 [00:00<?, ?it/s]
Plotting labels to runs/train/yolo_car_classification6/labels.jpg... 

AutoAnchor: 4.80 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Image sizes 224 train, 224 val
Using 4 dataloader workers
Logging results to runs/train/yolo_car_classification6
Starting training for 200 epochs...

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     0/199     1.11G   0.05393   0.02021    0.1235        46       224: 100% 204/204 [02:41<00:00,  1.26it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [03:48<00:00,  8.78s/it]
                 all       1629       1629    0.00523      0.606     0.0112    0.00769

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     1/199     1.73G   0.02907   0.01297    0.1209        46       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629    0.00503      0.996     0.0109    0.00893

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     2/199     1.73G   0.02766     0.011    0.1198        53       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629    0.00508          1     0.0108    0.00914

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     3/199     1.73G   0.02596   0.01018    0.1194        49       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629    0.00546      0.346     0.0102    0.00881

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     4/199     1.73G   0.02249  0.009612    0.1191        46       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629    0.00548      0.412     0.0114     0.0094

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     5/199     1.73G   0.01943  0.009082    0.1188        48       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.90it/s]
                 all       1629       1629    0.00632      0.393     0.0128     0.0114

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     6/199     1.73G   0.01775  0.008705    0.1186        52       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.73it/s]
                 all       1629       1629    0.00615      0.539     0.0123      0.011

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     7/199     1.73G   0.01672  0.008265    0.1184        49       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.72it/s]
                 all       1629       1629     0.0105      0.545      0.012     0.0107

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     8/199     1.73G   0.01618  0.008115    0.1183        49       224: 100% 204/204 [02:32<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.70it/s]
                 all       1629       1629    0.00551      0.581     0.0117     0.0105

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     9/199     1.73G    0.0157  0.008062    0.1182        54       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629    0.00617      0.627     0.0133     0.0119

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    10/199     1.73G   0.01529  0.007937    0.1181        55       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.66it/s]
                 all       1629       1629     0.0327      0.184      0.017     0.0149

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    11/199     1.73G   0.01509  0.007889    0.1177        47       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.042      0.259     0.0193     0.0166

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    12/199     1.73G   0.01519  0.007818    0.1173        45       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629     0.0329      0.256     0.0205     0.0183

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    13/199     1.73G   0.01499  0.007849    0.1167        51       224: 100% 204/204 [02:29<00:00,  1.37it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.65it/s]
                 all       1629       1629     0.0174      0.313     0.0219     0.0195

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    14/199     1.73G   0.01462  0.007693    0.1158        54       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629     0.0252      0.367     0.0229     0.0202

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    15/199     1.73G   0.01501  0.007726    0.1151        55       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.85it/s]
                 all       1629       1629     0.0309      0.419     0.0254     0.0226

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    16/199     1.73G   0.01484  0.007627    0.1143        42       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.038      0.284     0.0308     0.0275

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    17/199     1.73G   0.01501  0.007751    0.1133        45       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.82it/s]
                 all       1629       1629     0.0476      0.382      0.033     0.0292

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    18/199     1.73G   0.01517   0.00768    0.1124        45       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629     0.0223      0.418     0.0379     0.0332

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    19/199     1.73G   0.01526  0.007644    0.1113        48       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629     0.0208      0.581     0.0426      0.037

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    20/199     1.73G   0.01558  0.007689    0.1101        43       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.059      0.411     0.0452     0.0394

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    21/199     1.73G   0.01554  0.007742    0.1093        47       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.84it/s]
                 all       1629       1629     0.0689      0.475     0.0529     0.0465

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    22/199     1.73G   0.01553  0.007709    0.1085        53       224: 100% 204/204 [02:30<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629     0.0632      0.563     0.0508     0.0451

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    23/199     1.73G   0.01595  0.007666    0.1074        53       224: 100% 204/204 [02:30<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629     0.0706      0.569     0.0577     0.0507

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    24/199     1.73G   0.01579  0.007645    0.1066        55       224: 100% 204/204 [02:30<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.263      0.396     0.0605      0.054

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    25/199     1.73G   0.01588  0.007795    0.1059        58       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.156      0.428     0.0626     0.0552

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    26/199     1.73G   0.01594  0.007714    0.1051        50       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.68it/s]
                 all       1629       1629      0.212      0.426     0.0735     0.0655

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    27/199     1.73G    0.0159  0.007667    0.1041        50       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.82it/s]
                 all       1629       1629      0.215      0.417     0.0823     0.0727

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    28/199     1.73G   0.01598  0.007682    0.1035        48       224: 100% 204/204 [02:28<00:00,  1.37it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.168       0.48     0.0731     0.0643

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    29/199     1.73G   0.01572  0.007725     0.103        44       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.326      0.397      0.087     0.0773

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    30/199     1.73G   0.01603  0.007726    0.1022        48       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.217      0.476     0.0947     0.0833

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    31/199     1.73G   0.01606  0.007825    0.1012        44       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.213      0.482      0.102     0.0901

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    32/199     1.73G   0.01617   0.00782    0.1006        59       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629       0.21      0.509      0.108     0.0958

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    33/199     1.73G   0.01623  0.007797   0.09922        51       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.88it/s]
                 all       1629       1629      0.348      0.349      0.115      0.101

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    34/199     1.73G    0.0163  0.007879   0.09896        53       224: 100% 204/204 [02:28<00:00,  1.37it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.71it/s]
                 all       1629       1629      0.394      0.366      0.127      0.112

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    35/199     1.73G   0.01631  0.007851   0.09805        44       224: 100% 204/204 [02:28<00:00,  1.37it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.308      0.429      0.123       0.11

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    36/199     1.73G   0.01643  0.007845   0.09773        54       224: 100% 204/204 [02:30<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.433       0.32      0.135       0.12

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    37/199     1.73G   0.01644  0.007851   0.09658        41       224: 100% 204/204 [02:30<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.367      0.379      0.146      0.129

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    38/199     1.73G   0.01647  0.007915   0.09581        57       224: 100% 204/204 [02:30<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.81it/s]
                 all       1629       1629      0.291       0.47      0.153      0.137

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    39/199     1.73G   0.01667  0.007907   0.09477        49       224: 100% 204/204 [02:29<00:00,  1.36it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.81it/s]
                 all       1629       1629      0.278      0.509      0.179      0.161

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    40/199     1.73G   0.01648    0.0079   0.09437        44       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.73it/s]
                 all       1629       1629      0.267      0.507      0.179      0.158

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    41/199     1.73G   0.01676  0.007901   0.09345        46       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.81it/s]
                 all       1629       1629       0.24      0.558      0.196      0.175

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    42/199     1.73G   0.01667  0.007897   0.09249        50       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.318      0.413       0.21      0.189

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    43/199     1.73G   0.01683  0.007903   0.09186        58       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.215      0.577      0.213      0.189

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    44/199     1.73G   0.01678  0.007917   0.09101        52       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.318      0.439      0.214      0.191

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    45/199     1.73G   0.01698  0.007962   0.09026        50       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.289      0.479      0.234      0.208

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    46/199     1.73G   0.01699  0.007985   0.08948        44       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.84it/s]
                 all       1629       1629      0.237      0.585      0.247      0.221

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    47/199     1.73G   0.01706  0.007957   0.08891        47       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.355      0.441      0.262      0.235

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    48/199     1.73G   0.01695  0.007903   0.08811        50       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.278      0.526      0.264      0.237

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    49/199     1.73G   0.01714  0.007993   0.08738        54       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.276      0.543      0.286      0.256

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    50/199     1.73G   0.01714  0.007941   0.08676        48       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.92it/s]
                 all       1629       1629      0.346      0.509      0.296      0.264

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    51/199     1.73G   0.01714  0.007945   0.08566        55       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629      0.268      0.552      0.305      0.274

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    52/199     1.73G   0.01704  0.007991    0.0854        53       224: 100% 204/204 [02:35<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.261      0.566       0.31      0.277

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    53/199     1.73G   0.01725  0.008052   0.08479        50       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.82it/s]
                 all       1629       1629      0.249      0.618      0.332      0.298

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    54/199     1.73G   0.01706  0.007933   0.08397        46       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.89it/s]
                 all       1629       1629      0.343      0.504      0.348      0.312

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    55/199     1.73G   0.01717  0.007936   0.08329        47       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.82it/s]
                 all       1629       1629      0.285      0.586      0.361      0.325

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    56/199     1.73G   0.01727  0.007852   0.08224        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.73it/s]
                 all       1629       1629      0.332      0.578      0.374      0.336

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    57/199     1.73G   0.01735     0.008   0.08208        56       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.87it/s]
                 all       1629       1629      0.426      0.495      0.396      0.357

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    58/199     1.73G   0.01731  0.007908   0.08125        48       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.361       0.55      0.405      0.366

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    59/199     1.73G   0.01724  0.007919   0.08052        49       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.375      0.568      0.414      0.374

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    60/199     1.73G   0.01757  0.007998   0.07989        43       224: 100% 204/204 [02:32<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.327      0.591      0.407      0.364

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    61/199     1.73G   0.01746   0.00798   0.07928        43       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.72it/s]
                 all       1629       1629      0.397      0.544      0.426      0.386

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    62/199     1.73G   0.01746  0.007861   0.07871        56       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.355      0.591      0.446      0.404

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    63/199     1.73G   0.01769  0.007898   0.07762        45       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629       0.36       0.58      0.463      0.415

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    64/199     1.73G   0.01783  0.007961   0.07768        45       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.84it/s]
                 all       1629       1629      0.371        0.6      0.468      0.421

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    65/199     1.73G   0.01755  0.007911   0.07736        46       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.96it/s]
                 all       1629       1629      0.403      0.616       0.49      0.441

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    66/199     1.73G   0.01764  0.007978   0.07663        45       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.367      0.639      0.494      0.445

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    67/199     1.73G   0.01771   0.00806   0.07532        49       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.92it/s]
                 all       1629       1629      0.381      0.635      0.513      0.463

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    68/199     1.73G   0.01764  0.008013   0.07472        44       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.87it/s]
                 all       1629       1629      0.378       0.65      0.517      0.468

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    69/199     1.73G   0.01779  0.007938   0.07363        48       224: 100% 204/204 [02:33<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.523      0.547       0.53      0.481

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    70/199     1.73G   0.01788  0.008068   0.07366        48       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.73it/s]
                 all       1629       1629      0.455      0.603      0.533      0.483

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    71/199     1.73G   0.01762  0.007976   0.07261        48       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.65it/s]
                 all       1629       1629      0.464      0.606      0.539      0.486

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    72/199     1.73G   0.01782  0.008015   0.07225        52       224: 100% 204/204 [02:32<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.69it/s]
                 all       1629       1629      0.492      0.595      0.562      0.505

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    73/199     1.73G   0.01783  0.007993   0.07187        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.483      0.622      0.573      0.518

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    74/199     1.73G   0.01781   0.00801   0.07154        58       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.92it/s]
                 all       1629       1629      0.497      0.626      0.582      0.527

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    75/199     1.73G   0.01784  0.007937   0.07039        55       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.526      0.608      0.592      0.536

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    76/199     1.73G   0.01809  0.008141   0.07021        45       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.464      0.672      0.595      0.538

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    77/199     1.73G   0.01787  0.007976   0.06902        51       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.515      0.636      0.599      0.541

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    78/199     1.73G   0.01789   0.00801   0.06895        61       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.476      0.665      0.605      0.548

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    79/199     1.73G   0.01786  0.007944   0.06823        53       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.508      0.669      0.622      0.563

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    80/199     1.73G   0.01789  0.007962    0.0681        40       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.537      0.641      0.633      0.572

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    81/199     1.73G   0.01791  0.007976    0.0673        54       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.536      0.648      0.638      0.577

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    82/199     1.73G   0.01789  0.008019   0.06695        41       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.504      0.712       0.65       0.59

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    83/199     1.73G   0.01799  0.007968    0.0657        53       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.484      0.739      0.659      0.595

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    84/199     1.73G   0.01798  0.008016   0.06558        41       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.84it/s]
                 all       1629       1629      0.495      0.715      0.658      0.596

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    85/199     1.73G   0.01794  0.008048   0.06518        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.469      0.756      0.668      0.605

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    86/199     1.73G   0.01806  0.007973    0.0642        50       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.591      0.679      0.678      0.614

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    87/199     1.73G   0.01797  0.007965   0.06415        51       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.585      0.679      0.685      0.622

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    88/199     1.73G   0.01807  0.007981   0.06383        49       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.489      0.755       0.69      0.625

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    89/199     1.73G   0.01794  0.007989   0.06326        52       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.471      0.781      0.698      0.634

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    90/199     1.73G   0.01777   0.00793   0.06251        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629      0.522      0.736      0.699      0.636

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    91/199     1.73G   0.01804  0.007902   0.06179        50       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.555      0.732      0.713      0.649

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    92/199     1.73G   0.01805  0.008036   0.06141        52       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.588      0.697      0.714       0.65

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    93/199     1.73G   0.01812  0.007914   0.06063        46       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.574      0.715      0.715      0.651

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    94/199     1.73G   0.01795  0.007946   0.06061        57       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.579      0.723      0.723      0.658

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    95/199     1.73G   0.01807  0.007848   0.05923        50       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.72it/s]
                 all       1629       1629      0.592      0.731      0.728      0.665

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    96/199     1.73G   0.01791  0.008023   0.05915        42       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629      0.557      0.757      0.736      0.672

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    97/199     1.73G   0.01777   0.00787   0.05844        43       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.85it/s]
                 all       1629       1629        0.6      0.734      0.745      0.682

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    98/199     1.73G   0.01785   0.00782   0.05824        47       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.81it/s]
                 all       1629       1629      0.624      0.725      0.749      0.682

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    99/199     1.73G   0.01783  0.007909   0.05749        41       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.636      0.733      0.757       0.69

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   100/199     1.73G    0.0178   0.00792   0.05712        51       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.71it/s]
                 all       1629       1629      0.609      0.756       0.76      0.695

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   101/199     1.73G   0.01776  0.007919   0.05681        56       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.88it/s]
                 all       1629       1629      0.644      0.734      0.761      0.696

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   102/199     1.73G   0.01815  0.008006   0.05566        47       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.82it/s]
                 all       1629       1629      0.649      0.734      0.764        0.7

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   103/199     1.73G   0.01788  0.007937   0.05644        54       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.66it/s]
                 all       1629       1629       0.64      0.748      0.771      0.704

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   104/199     1.73G   0.01776  0.007878   0.05519        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.625      0.774      0.774      0.708

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   105/199     1.73G   0.01768  0.007875   0.05542        53       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.86it/s]
                 all       1629       1629      0.627      0.768      0.776      0.709

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   106/199     1.73G    0.0176  0.007773    0.0545        52       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.634      0.774      0.784      0.715

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   107/199     1.73G   0.01756  0.007809   0.05455        52       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.614      0.801      0.787      0.718

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   108/199     1.73G   0.01767  0.007878   0.05348        52       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629       0.63      0.792      0.791      0.723

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   109/199     1.73G   0.01782  0.007859   0.05266        58       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.89it/s]
                 all       1629       1629      0.627      0.795      0.795      0.727

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   110/199     1.73G   0.01767  0.007814   0.05219        50       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.659      0.776      0.799      0.731

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   111/199     1.73G   0.01759  0.007875   0.05273        58       224: 100% 204/204 [02:31<00:00,  1.35it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.686      0.769      0.804      0.735

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   112/199     1.73G   0.01767  0.007867   0.05193        49       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629      0.707      0.763      0.807      0.738

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   113/199     1.73G   0.01761  0.007742   0.05144        39       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.84it/s]
                 all       1629       1629      0.715      0.766      0.809       0.74

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   114/199     1.73G   0.01747   0.00782   0.05148        42       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.724      0.769      0.812      0.744

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   115/199     1.73G   0.01759  0.007872   0.05046        50       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.695      0.787      0.815      0.746

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   116/199     1.73G   0.01728   0.00778   0.05056        52       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.79it/s]
                 all       1629       1629      0.699      0.789      0.818      0.748

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   117/199     1.73G   0.01734  0.007827   0.05007        52       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.733      0.777      0.823      0.751

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   118/199     1.73G   0.01733  0.007795   0.04946        42       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.85it/s]
                 all       1629       1629      0.724      0.785      0.825      0.753

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   119/199     1.73G   0.01732  0.007777   0.04823        47       224: 100% 204/204 [02:31<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.73it/s]
                 all       1629       1629      0.714      0.804      0.829      0.758

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   120/199     1.73G   0.01715  0.007648   0.04842        50       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.715      0.803      0.833      0.761

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   121/199     1.73G   0.01729  0.007723   0.04748        47       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.723        0.8      0.834      0.761

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   122/199     1.73G   0.01733  0.007744   0.04742        57       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.73it/s]
                 all       1629       1629      0.744      0.786      0.834      0.762

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   123/199     1.73G   0.01734  0.007739   0.04702        51       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.734      0.797      0.835      0.763

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   124/199     1.73G   0.01707  0.007655    0.0469        49       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.752      0.789      0.838      0.766

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   125/199     1.73G   0.01698  0.007668   0.04659        47       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.81it/s]
                 all       1629       1629      0.773      0.783      0.839      0.766

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   126/199     1.73G    0.0171  0.007738   0.04598        54       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.776      0.782      0.841      0.769

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   127/199     1.73G   0.01707  0.007626   0.04543        54       224: 100% 204/204 [02:33<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.63it/s]
                 all       1629       1629       0.78      0.787      0.842      0.771

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   128/199     1.73G   0.01699  0.007651   0.04558        48       224: 100% 204/204 [02:37<00:00,  1.30it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.787      0.784      0.844      0.772

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   129/199     1.73G   0.01684  0.007549   0.04496        53       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:16<00:00,  1.62it/s]
                 all       1629       1629      0.787      0.789      0.846      0.776

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   130/199     1.73G    0.0169  0.007587   0.04372        55       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.86it/s]
                 all       1629       1629      0.769      0.804      0.847      0.776

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   131/199     1.73G   0.01673  0.007659   0.04434        51       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.69it/s]
                 all       1629       1629      0.778      0.802      0.849      0.777

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   132/199     1.73G   0.01652   0.00763   0.04401        53       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.76it/s]
                 all       1629       1629      0.795      0.794       0.85      0.778

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   133/199     1.73G   0.01662  0.007587   0.04369        52       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.77it/s]
                 all       1629       1629      0.797      0.796      0.853      0.781

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   134/199     1.73G   0.01674  0.007632   0.04304        53       224: 100% 204/204 [02:36<00:00,  1.30it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629        0.8      0.797      0.856      0.784

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   135/199     1.73G   0.01652  0.007645   0.04226        47       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.78it/s]
                 all       1629       1629      0.767      0.823      0.857      0.786

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   136/199     1.73G   0.01639  0.007479   0.04241        54       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.804      0.797      0.859      0.787

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   137/199     1.73G   0.01646   0.00753   0.04218        45       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.83it/s]
                 all       1629       1629      0.801        0.8      0.861      0.789

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   138/199     1.73G   0.01635  0.007481    0.0418        52       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.74it/s]
                 all       1629       1629      0.797      0.807      0.862       0.79

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   139/199     1.73G   0.01638  0.007541   0.04129        57       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.73it/s]
                 all       1629       1629      0.801       0.81      0.863       0.79

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   140/199     1.73G   0.01621  0.007493    0.0407        45       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.75it/s]
                 all       1629       1629      0.806      0.806      0.865      0.792

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   141/199     1.73G   0.01617  0.007479    0.0406        50       224: 100% 204/204 [02:34<00:00,  1.32it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629       0.81      0.808      0.866      0.793

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   142/199     1.73G    0.0163  0.007437   0.03962        44       224: 100% 204/204 [02:32<00:00,  1.34it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.812      0.807      0.867      0.794

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   143/199     1.73G   0.01601  0.007462    0.0403        49       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00,  1.80it/s]
                 all       1629       1629      0.816      0.807      0.869      0.795

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   144/199     1.73G   0.01613  0.007542   0.03942        54       224: 100% 204/204 [02:35<00:00,  1.31it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00,  1.91it/s]
                 all       1629       1629      0.812       0.81      0.871      0.797

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   145/199     1.73G   0.01598  0.007501   0.03879        54       224: 100% 204/204 [02:33<00:00,  1.33it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00,  1.68it/s]
                 all       1629       1629      0.808      0.813      0.871      0.798

     Epoch   gpu_mem       box       obj       cls    labels  img_size
   146/199     1.73G   0.01602  0.007438   0.03887        88       224:  96% 195/204 [02:26<00:04,  1.97it/s]

!ls

sample_data


!python val.py --img 224 --weights /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights/best.pt --data carData.yaml --task test --name yolo_car_class_det

val: data=/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/data/carData.yaml, weights=['/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights/best.pt'], batch_size=32, imgsz=224, conf_thres=0.001, iou_thres=0.6, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=yolo_car_class_det, exist_ok=False, half=False, dnn=False
YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)

Fusing layers... 
Model Summary: 213 layers, 7538737 parameters, 0 gradients
test: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/labels.cache' images and labels... 8040 found, 0 missing, 0 empty, 0 corrupt: 100% 8040/8040 [00:00<?, ?it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 252/252 [00:58<00:00,  4.32it/s]
                 all       8040       8040      0.789       0.86      0.881      0.802
Speed: 0.0ms pre-process, 1.0ms inference, 1.0ms NMS per image at shape (32, 3, 224, 224)
Results saved to runs/val/yolo_car_class_det5


!python export.py --weights runs/train/yolo_car_classification6/weights/best.pt --img 224 --batch 1  # export at 640x640 with batch size 1

export: data=data/coco128.yaml, weights=['runs/train/yolo_car_classification6/weights/best.pt'], imgsz=[224], batch_size=1, device=cpu, half=False, inplace=False, train=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CPU

Fusing layers... 
Model Summary: 213 layers, 7538737 parameters, 0 gradients

PyTorch: starting from runs/train/yolo_car_classification6/weights/best.pt (60.9 MB)

TorchScript: starting export with torch 1.10.0+cu111...
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
TorchScript: export success, saved as runs/train/yolo_car_classification6/weights/best.torchscript (30.5 MB)
requirements: onnx not found and is required by YOLOv5, attempting auto-update...
Collecting onnx
  Downloading onnx-1.10.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.7 MB)
Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx) (1.19.5)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx) (1.15.0)
Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx) (3.17.3)
Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx) (3.10.0.2)
Installing collected packages: onnx
Successfully installed onnx-1.10.2

requirements: 1 package updated per ['onnx']
requirements: ⚠️ Restart runtime or rerun command for updates to take effect


ONNX: starting export with onnx 1.10.2...
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
ONNX: export success, saved as runs/train/yolo_car_classification6/weights/best.onnx (30.2 MB)

Export complete (8.51s)
Results saved to /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights
Visualize with https://netron.app
Detect with `python detect.py --weights runs/train/yolo_car_classification6/weights/best.onnx` or `model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train/yolo_car_classification6/weights/best.onnx')
Validate with `python val.py --weights runs/train/yolo_car_classification6/weights/best.onnx`


IMAGE_PATHS = list(df_test_ann['filepath'])
IMAGE_PATHS

['/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg',
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 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Cobalt SS 2010/00909.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Extended Cab 2012/00910.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Challenger SRT8 2011/00911.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford GT Coupe 2006/00912.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura RL Sedan 2012/00913.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Durango SUV 2007/00914.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00915.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Savana Van 2012/00916.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Infiniti G Coupe IPL 2012/00917.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00918.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Rolls-Royce Ghost Sedan 2012/00919.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW 6 Series Convertible 2007/00920.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota 4Runner SUV 2012/00921.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veloster Hatchback 2012/00922.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari California Convertible 2012/00923.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Geo Metro Convertible 1993/00924.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caravan Minivan 1997/00925.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Savana Van 2012/00926.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Scion xD Hatchback 2012/00927.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S4 Sedan 2012/00928.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi R8 Coupe 2012/00929.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Corvette ZR1 2012/00930.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Durango SUV 2007/00931.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Avalanche Crew Cab 2012/00932.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/smart fortwo Convertible 2012/00933.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Tucson SUV 2012/00934.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover LR2 SUV 2012/00935.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00936.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00937.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Gallardo LP 570-4 Superleggera 2012/00938.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler 300 SRT-8 2010/00939.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Accord Coupe 2012/00940.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet TrailBlazer SS 2009/00941.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Expedition EL SUV 2009/00942.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00943.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Monte Carlo Coupe 2007/00944.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Sebring Convertible 2010/00945.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW ActiveHybrid 5 Sedan 2012/00946.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veracruz SUV 2012/00947.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Mulsanne Sedan 2011/00948.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi 100 Sedan 1994/00949.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00950.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Crew Cab 2010/00951.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Camaro Convertible 2012/00952.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Regular Cab 2012/00953.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo 240 Sedan 1993/00954.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Cobalt SS 2010/00955.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Monte Carlo Coupe 2007/00956.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi R8 Coupe 2012/00957.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00958.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ram C-V Cargo Van Minivan 2012/00959.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Beetle Hatchback 2012/00960.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00961.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00962.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac Escalade EXT Crew Cab 2007/00963.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2012/00964.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00965.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2012/00966.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW ActiveHybrid 5 Sedan 2012/00967.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/FIAT 500 Abarth 2012/00968.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Durango SUV 2007/00969.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW 3 Series Sedan 2012/00970.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz C-Class Sedan 2012/00971.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/McLaren MP4-12C Coupe 2012/00972.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Corvette ZR1 2012/00973.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Classic Extended Cab 2007/00974.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Rolls-Royce Phantom Sedan 2012/00975.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura ZDX Hatchback 2012/00976.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 2012/00977.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW M5 Sedan 2010/00978.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Aston Martin Virage Coupe 2012/00979.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mitsubishi Lancer Sedan 2012/00980.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Corvette Ron Fellows Edition Z06 2007/00981.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veracruz SUV 2012/00982.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Journey SUV 2012/00983.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00984.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Cobalt SS 2010/00985.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00986.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2007/00987.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2012/00988.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota 4Runner SUV 2012/00989.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Fisker Karma Sedan 2012/00990.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura ZDX Hatchback 2012/00991.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Spyker C8 Convertible 2009/00992.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Sonic Sedan 2012/00993.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00994.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Impala Sedan 2007/00995.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00996.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi TTS Coupe 2012/00997.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Maybach Landaulet Convertible 2012/00998.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford F-150 Regular Cab 2012/00999.jpg',
 '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/01000.jpg',
 ...]


os.getcwd()

'/content/drive/MyDrive/capstone/tensorflow/models/research'


%cd ..

/content/drive/MyDrive/capstone/tensorflow


%cd workspace/training-ssd/exported-models/my_model/

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/exported-models/my_model


#PATH_TO_MODEL_DIR = "workspace/training-sdd/exported-models/my_model"
#os.chdir(PATH_TO_MODEL_DIR)
import time
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils

#PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "saved_model"

print('Loading model...', end='')
start_time = time.time()

# Load saved model and build the detection function
detect_fn = tf.saved_model.load("saved_model")

end_time = time.time()
elapsed_time = end_time - start_time
print('Done! Took {} seconds'.format(elapsed_time))

Loading model...Done! Took 23.845096588134766 seconds


def download_labels(filename):
    #base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'
    #label_dir = tf.keras.utils.get_file(fname=filename,
    #                                    origin=base_url + filename,
    #                                    untar=False)
    label_dir = os.path.join("/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/annotations",filename)
    return str(label_dir)

LABEL_FILENAME = 'label_map.pbtxt'
PATH_TO_LABELS = download_labels(LABEL_FILENAME)


PATH_TO_LABELS

'/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/annotations/label_map.pbtxt'


category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
                                                                    use_display_name=True)


category_index

{1: {'id': 1, 'name': 'AM General Hummer SUV 2000'},
 2: {'id': 2, 'name': 'Acura RL Sedan 2012'},
 3: {'id': 3, 'name': 'Acura TL Sedan 2012'},
 4: {'id': 4, 'name': 'Acura TL Type-S 2008'},
 5: {'id': 5, 'name': 'Acura TSX Sedan 2012'},
 6: {'id': 6, 'name': 'Acura Integra Type R 2001'},
 7: {'id': 7, 'name': 'Acura ZDX Hatchback 2012'},
 8: {'id': 8, 'name': 'Aston Martin V8 Vantage Convertible 2012'},
 9: {'id': 9, 'name': 'Aston Martin V8 Vantage Coupe 2012'},
 10: {'id': 10, 'name': 'Aston Martin Virage Convertible 2012'},
 11: {'id': 11, 'name': 'Aston Martin Virage Coupe 2012'},
 12: {'id': 12, 'name': 'Audi RS 4 Convertible 2008'},
 13: {'id': 13, 'name': 'Audi A5 Coupe 2012'},
 14: {'id': 14, 'name': 'Audi TTS Coupe 2012'},
 15: {'id': 15, 'name': 'Audi R8 Coupe 2012'},
 16: {'id': 16, 'name': 'Audi V8 Sedan 1994'},
 17: {'id': 17, 'name': 'Audi 100 Sedan 1994'},
 18: {'id': 18, 'name': 'Audi 100 Wagon 1994'},
 19: {'id': 19, 'name': 'Audi TT Hatchback 2011'},
 20: {'id': 20, 'name': 'Audi S6 Sedan 2011'},
 21: {'id': 21, 'name': 'Audi S5 Convertible 2012'},
 22: {'id': 22, 'name': 'Audi S5 Coupe 2012'},
 23: {'id': 23, 'name': 'Audi S4 Sedan 2012'},
 24: {'id': 24, 'name': 'Audi S4 Sedan 2007'},
 25: {'id': 25, 'name': 'Audi TT RS Coupe 2012'},
 26: {'id': 26, 'name': 'BMW ActiveHybrid 5 Sedan 2012'},
 27: {'id': 27, 'name': 'BMW 1 Series Convertible 2012'},
 28: {'id': 28, 'name': 'BMW 1 Series Coupe 2012'},
 29: {'id': 29, 'name': 'BMW 3 Series Sedan 2012'},
 30: {'id': 30, 'name': 'BMW 3 Series Wagon 2012'},
 31: {'id': 31, 'name': 'BMW 6 Series Convertible 2007'},
 32: {'id': 32, 'name': 'BMW X5 SUV 2007'},
 33: {'id': 33, 'name': 'BMW X6 SUV 2012'},
 34: {'id': 34, 'name': 'BMW M3 Coupe 2012'},
 35: {'id': 35, 'name': 'BMW M5 Sedan 2010'},
 36: {'id': 36, 'name': 'BMW M6 Convertible 2010'},
 37: {'id': 37, 'name': 'BMW X3 SUV 2012'},
 38: {'id': 38, 'name': 'BMW Z4 Convertible 2012'},
 39: {'id': 39,
  'name': 'Bentley Continental Supersports Conv. Convertible 2012'},
 40: {'id': 40, 'name': 'Bentley Arnage Sedan 2009'},
 41: {'id': 41, 'name': 'Bentley Mulsanne Sedan 2011'},
 42: {'id': 42, 'name': 'Bentley Continental GT Coupe 2012'},
 43: {'id': 43, 'name': 'Bentley Continental GT Coupe 2007'},
 44: {'id': 44, 'name': 'Bentley Continental Flying Spur Sedan 2007'},
 45: {'id': 45, 'name': 'Bugatti Veyron 16.4 Convertible 2009'},
 46: {'id': 46, 'name': 'Bugatti Veyron 16.4 Coupe 2009'},
 47: {'id': 47, 'name': 'Buick Regal GS 2012'},
 48: {'id': 48, 'name': 'Buick Rainier SUV 2007'},
 49: {'id': 49, 'name': 'Buick Verano Sedan 2012'},
 50: {'id': 50, 'name': 'Buick Enclave SUV 2012'},
 51: {'id': 51, 'name': 'Cadillac CTS-V Sedan 2012'},
 52: {'id': 52, 'name': 'Cadillac SRX SUV 2012'},
 53: {'id': 53, 'name': 'Cadillac Escalade EXT Crew Cab 2007'},
 54: {'id': 54, 'name': 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012'},
 55: {'id': 55, 'name': 'Chevrolet Corvette Convertible 2012'},
 56: {'id': 56, 'name': 'Chevrolet Corvette ZR1 2012'},
 57: {'id': 57, 'name': 'Chevrolet Corvette Ron Fellows Edition Z06 2007'},
 58: {'id': 58, 'name': 'Chevrolet Traverse SUV 2012'},
 59: {'id': 59, 'name': 'Chevrolet Camaro Convertible 2012'},
 60: {'id': 60, 'name': 'Chevrolet HHR SS 2010'},
 61: {'id': 61, 'name': 'Chevrolet Impala Sedan 2007'},
 62: {'id': 62, 'name': 'Chevrolet Tahoe Hybrid SUV 2012'},
 63: {'id': 63, 'name': 'Chevrolet Sonic Sedan 2012'},
 64: {'id': 64, 'name': 'Chevrolet Express Cargo Van 2007'},
 65: {'id': 65, 'name': 'Chevrolet Avalanche Crew Cab 2012'},
 66: {'id': 66, 'name': 'Chevrolet Cobalt SS 2010'},
 67: {'id': 67, 'name': 'Chevrolet Malibu Hybrid Sedan 2010'},
 68: {'id': 68, 'name': 'Chevrolet TrailBlazer SS 2009'},
 69: {'id': 69, 'name': 'Chevrolet Silverado 2500HD Regular Cab 2012'},
 70: {'id': 70, 'name': 'Chevrolet Silverado 1500 Classic Extended Cab 2007'},
 71: {'id': 71, 'name': 'Chevrolet Express Van 2007'},
 72: {'id': 72, 'name': 'Chevrolet Monte Carlo Coupe 2007'},
 73: {'id': 73, 'name': 'Chevrolet Malibu Sedan 2007'},
 74: {'id': 74, 'name': 'Chevrolet Silverado 1500 Extended Cab 2012'},
 75: {'id': 75, 'name': 'Chevrolet Silverado 1500 Regular Cab 2012'},
 76: {'id': 76, 'name': 'Chrysler Aspen SUV 2009'},
 77: {'id': 77, 'name': 'Chrysler Sebring Convertible 2010'},
 78: {'id': 78, 'name': 'Chrysler Town and Country Minivan 2012'},
 79: {'id': 79, 'name': 'Chrysler 300 SRT-8 2010'},
 80: {'id': 80, 'name': 'Chrysler Crossfire Convertible 2008'},
 81: {'id': 81, 'name': 'Chrysler PT Cruiser Convertible 2008'},
 82: {'id': 82, 'name': 'Daewoo Nubira Wagon 2002'},
 83: {'id': 83, 'name': 'Dodge Caliber Wagon 2012'},
 84: {'id': 84, 'name': 'Dodge Caliber Wagon 2007'},
 85: {'id': 85, 'name': 'Dodge Caravan Minivan 1997'},
 86: {'id': 86, 'name': 'Dodge Ram Pickup 3500 Crew Cab 2010'},
 87: {'id': 87, 'name': 'Dodge Ram Pickup 3500 Quad Cab 2009'},
 88: {'id': 88, 'name': 'Dodge Sprinter Cargo Van 2009'},
 89: {'id': 89, 'name': 'Dodge Journey SUV 2012'},
 90: {'id': 90, 'name': 'Dodge Dakota Crew Cab 2010'},
 91: {'id': 91, 'name': 'Dodge Dakota Club Cab 2007'},
 92: {'id': 92, 'name': 'Dodge Magnum Wagon 2008'},
 93: {'id': 93, 'name': 'Dodge Challenger SRT8 2011'},
 94: {'id': 94, 'name': 'Dodge Durango SUV 2012'},
 95: {'id': 95, 'name': 'Dodge Durango SUV 2007'},
 96: {'id': 96, 'name': 'Dodge Charger Sedan 2012'},
 97: {'id': 97, 'name': 'Dodge Charger SRT-8 2009'},
 98: {'id': 98, 'name': 'Eagle Talon Hatchback 1998'},
 99: {'id': 99, 'name': 'FIAT 500 Abarth 2012'},
 100: {'id': 100, 'name': 'FIAT 500 Convertible 2012'},
 101: {'id': 101, 'name': 'Ferrari FF Coupe 2012'},
 102: {'id': 102, 'name': 'Ferrari California Convertible 2012'},
 103: {'id': 103, 'name': 'Ferrari 458 Italia Convertible 2012'},
 104: {'id': 104, 'name': 'Ferrari 458 Italia Coupe 2012'},
 105: {'id': 105, 'name': 'Fisker Karma Sedan 2012'},
 106: {'id': 106, 'name': 'Ford F-450 Super Duty Crew Cab 2012'},
 107: {'id': 107, 'name': 'Ford Mustang Convertible 2007'},
 108: {'id': 108, 'name': 'Ford Freestar Minivan 2007'},
 109: {'id': 109, 'name': 'Ford Expedition EL SUV 2009'},
 110: {'id': 110, 'name': 'Ford Edge SUV 2012'},
 111: {'id': 111, 'name': 'Ford Ranger SuperCab 2011'},
 112: {'id': 112, 'name': 'Ford GT Coupe 2006'},
 113: {'id': 113, 'name': 'Ford F-150 Regular Cab 2012'},
 114: {'id': 114, 'name': 'Ford F-150 Regular Cab 2007'},
 115: {'id': 115, 'name': 'Ford Focus Sedan 2007'},
 116: {'id': 116, 'name': 'Ford E-Series Wagon Van 2012'},
 117: {'id': 117, 'name': 'Ford Fiesta Sedan 2012'},
 118: {'id': 118, 'name': 'GMC Terrain SUV 2012'},
 119: {'id': 119, 'name': 'GMC Savana Van 2012'},
 120: {'id': 120, 'name': 'GMC Yukon Hybrid SUV 2012'},
 121: {'id': 121, 'name': 'GMC Acadia SUV 2012'},
 122: {'id': 122, 'name': 'GMC Canyon Extended Cab 2012'},
 123: {'id': 123, 'name': 'Geo Metro Convertible 1993'},
 124: {'id': 124, 'name': 'HUMMER H3T Crew Cab 2010'},
 125: {'id': 125, 'name': 'HUMMER H2 SUT Crew Cab 2009'},
 126: {'id': 126, 'name': 'Honda Odyssey Minivan 2012'},
 127: {'id': 127, 'name': 'Honda Odyssey Minivan 2007'},
 128: {'id': 128, 'name': 'Honda Accord Coupe 2012'},
 129: {'id': 129, 'name': 'Honda Accord Sedan 2012'},
 130: {'id': 130, 'name': 'Hyundai Veloster Hatchback 2012'},
 131: {'id': 131, 'name': 'Hyundai Santa Fe SUV 2012'},
 132: {'id': 132, 'name': 'Hyundai Tucson SUV 2012'},
 133: {'id': 133, 'name': 'Hyundai Veracruz SUV 2012'},
 134: {'id': 134, 'name': 'Hyundai Sonata Hybrid Sedan 2012'},
 135: {'id': 135, 'name': 'Hyundai Elantra Sedan 2007'},
 136: {'id': 136, 'name': 'Hyundai Accent Sedan 2012'},
 137: {'id': 137, 'name': 'Hyundai Genesis Sedan 2012'},
 138: {'id': 138, 'name': 'Hyundai Sonata Sedan 2012'},
 139: {'id': 139, 'name': 'Hyundai Elantra Touring Hatchback 2012'},
 140: {'id': 140, 'name': 'Hyundai Azera Sedan 2012'},
 141: {'id': 141, 'name': 'Infiniti G Coupe IPL 2012'},
 142: {'id': 142, 'name': 'Infiniti QX56 SUV 2011'},
 143: {'id': 143, 'name': 'Isuzu Ascender SUV 2008'},
 144: {'id': 144, 'name': 'Jaguar XK XKR 2012'},
 145: {'id': 145, 'name': 'Jeep Patriot SUV 2012'},
 146: {'id': 146, 'name': 'Jeep Wrangler SUV 2012'},
 147: {'id': 147, 'name': 'Jeep Liberty SUV 2012'},
 148: {'id': 148, 'name': 'Jeep Grand Cherokee SUV 2012'},
 149: {'id': 149, 'name': 'Jeep Compass SUV 2012'},
 150: {'id': 150, 'name': 'Lamborghini Reventon Coupe 2008'},
 151: {'id': 151, 'name': 'Lamborghini Aventador Coupe 2012'},
 152: {'id': 152, 'name': 'Lamborghini Gallardo LP 570-4 Superleggera 2012'},
 153: {'id': 153, 'name': 'Lamborghini Diablo Coupe 2001'},
 154: {'id': 154, 'name': 'Land Rover Range Rover SUV 2012'},
 155: {'id': 155, 'name': 'Land Rover LR2 SUV 2012'},
 156: {'id': 156, 'name': 'Lincoln Town Car Sedan 2011'},
 157: {'id': 157, 'name': 'MINI Cooper Roadster Convertible 2012'},
 158: {'id': 158, 'name': 'Maybach Landaulet Convertible 2012'},
 159: {'id': 159, 'name': 'Mazda Tribute SUV 2011'},
 160: {'id': 160, 'name': 'McLaren MP4-12C Coupe 2012'},
 161: {'id': 161, 'name': 'Mercedes-Benz 300-Class Convertible 1993'},
 162: {'id': 162, 'name': 'Mercedes-Benz C-Class Sedan 2012'},
 163: {'id': 163, 'name': 'Mercedes-Benz SL-Class Coupe 2009'},
 164: {'id': 164, 'name': 'Mercedes-Benz E-Class Sedan 2012'},
 165: {'id': 165, 'name': 'Mercedes-Benz S-Class Sedan 2012'},
 166: {'id': 166, 'name': 'Mercedes-Benz Sprinter Van 2012'},
 167: {'id': 167, 'name': 'Mitsubishi Lancer Sedan 2012'},
 168: {'id': 168, 'name': 'Nissan Leaf Hatchback 2012'},
 169: {'id': 169, 'name': 'Nissan NV Passenger Van 2012'},
 170: {'id': 170, 'name': 'Nissan Juke Hatchback 2012'},
 171: {'id': 171, 'name': 'Nissan 240SX Coupe 1998'},
 172: {'id': 172, 'name': 'Plymouth Neon Coupe 1999'},
 173: {'id': 173, 'name': 'Porsche Panamera Sedan 2012'},
 174: {'id': 174, 'name': 'Ram C-V Cargo Van Minivan 2012'},
 175: {'id': 175,
  'name': 'Rolls-Royce Phantom Drophead Coupe Convertible 2012'},
 176: {'id': 176, 'name': 'Rolls-Royce Ghost Sedan 2012'},
 177: {'id': 177, 'name': 'Rolls-Royce Phantom Sedan 2012'},
 178: {'id': 178, 'name': 'Scion xD Hatchback 2012'},
 179: {'id': 179, 'name': 'Spyker C8 Convertible 2009'},
 180: {'id': 180, 'name': 'Spyker C8 Coupe 2009'},
 181: {'id': 181, 'name': 'Suzuki Aerio Sedan 2007'},
 182: {'id': 182, 'name': 'Suzuki Kizashi Sedan 2012'},
 183: {'id': 183, 'name': 'Suzuki SX4 Hatchback 2012'},
 184: {'id': 184, 'name': 'Suzuki SX4 Sedan 2012'},
 185: {'id': 185, 'name': 'Tesla Model S Sedan 2012'},
 186: {'id': 186, 'name': 'Toyota Sequoia SUV 2012'},
 187: {'id': 187, 'name': 'Toyota Camry Sedan 2012'},
 188: {'id': 188, 'name': 'Toyota Corolla Sedan 2012'},
 189: {'id': 189, 'name': 'Toyota 4Runner SUV 2012'},
 190: {'id': 190, 'name': 'Volkswagen Golf Hatchback 2012'},
 191: {'id': 191, 'name': 'Volkswagen Golf Hatchback 1991'},
 192: {'id': 192, 'name': 'Volkswagen Beetle Hatchback 2012'},
 193: {'id': 193, 'name': 'Volvo C30 Hatchback 2012'},
 194: {'id': 194, 'name': 'Volvo 240 Sedan 1993'},
 195: {'id': 195, 'name': 'Volvo XC90 SUV 2007'},
 196: {'id': 196, 'name': 'smart fortwo Convertible 2012'}}


import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings

def load_image_into_numpy_array(path):
    """Load an image from file into a numpy array.

    Puts image into numpy array to feed into tensorflow graph.
    Note that by convention we put it into a numpy array with shape
    (height, width, channels), where channels=3 for RGB.

    Args:
      path: the file path to the image

    Returns:
      uint8 numpy array with shape (img_height, img_width, 3)
    """
    return np.array(Image.open(path))


for image_path in IMAGE_PATHS:

    print('Running inference for {}... '.format(image_path), end='')

    image_np = load_image_into_numpy_array(image_path)

    # Things to try:
    # Flip horizontally
    # image_np = np.fliplr(image_np).copy()

    # Convert image to grayscale
    # image_np = np.tile(
    #     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)

    # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
    input_tensor = tf.convert_to_tensor(image_np)
    # The model expects a batch of images, so add an axis with `tf.newaxis`.
    input_tensor = input_tensor[tf.newaxis, ...]

    # input_tensor = np.expand_dims(image_np, 0)
    detections = detect_fn(input_tensor)

    # All outputs are batches tensors.
    # Convert to numpy arrays, and take index [0] to remove the batch dimension.
    # We're only interested in the first num_detections.
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy()
                   for key, value in detections.items()}
    detections['num_detections'] = num_detections

    # detection_classes should be ints.
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
          image_np_with_detections,
          detections['detection_boxes'],
          detections['detection_classes'],
          detections['detection_scores'],
          category_index,
          use_normalized_coordinates=True,
          max_boxes_to_draw=200,
          min_score_thresh=.30,
          agnostic_mode=False)

    plt.figure()
    plt.imshow(image_np_with_detections)
    print('Done')
plt.show()

Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00008.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00009.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet HHR SS 2010/00010.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Buick Verano Sedan 2012/00011.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Freestar Minivan 2007/00012.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford E-Series Wagon Van 2012/00013.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Elantra Sedan 2007/00014.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00015.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00016.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover Range Rover SUV 2012/00017.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00018.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S5 Coupe 2012/00019.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00020.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Aventador Coupe 2012/00021.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00022.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler PT Cruiser Convertible 2008/00023.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Mustang Convertible 2007/00024.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00025.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00026.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00027.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo XC90 SUV 2007/00028.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Club Cab 2007/00029.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TSX Sedan 2012/00030.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00031.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Grand Cherokee SUV 2012/00032.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00033.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental Flying Spur Sedan 2007/00034.jpg...

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-70-5c2ce9f3fedc> in <module>()
     41 
     42     # input_tensor = np.expand_dims(image_np, 0)
---> 43     detections = detect_fn(input_tensor)
     44 
     45     # All outputs are batches tensors.

/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in _call_attribute(instance, *args, **kwargs)
    699 
    700 def _call_attribute(instance, *args, **kwargs):
--> 701   return instance.__call__(*args, **kwargs)
    702 
    703 

/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs)
    151     except Exception as e:
    152       filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153       raise e.with_traceback(filtered_tb) from None
    154     finally:
    155       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _convert_inputs_to_signature(inputs, input_signature, flat_input_signature)
   2980       flat_input_signature,
   2981       flatten_inputs)):
-> 2982     raise ValueError("Python inputs incompatible with input_signature:\n"
   2983                      f"{format_error_message(inputs, input_signature)}.")
   2984 

ValueError: Python inputs incompatible with input_signature:
  inputs: (
    tf.Tensor(
[[[ 87  87  87 ... 151 151 151]
  [ 86  85  85 ... 152 152 152]
  [ 82  82  82 ... 152 152 152]
  ...
  [133 133 133 ... 133 133 133]
  [133 133 133 ... 133 133 133]
  [133 133 133 ... 133 133 133]]], shape=(1, 400, 600), dtype=uint8))
  input_signature: (
    TensorSpec(shape=(1, None, None, 3), dtype=tf.uint8, name='input_tensor')).


%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd


os.getcwd()

'/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd'

!ls

 annotations	       images		  'pre-trained models'
 exported-models       model_main_tf2.py
 exporter_main_v2.py   models


%cd exported-models/my_model_rcnn/

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/exported-models/my_model_rcnn

!ls

checkpoint  pipeline.config  saved_model


import time
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
print('Loading model...', end='')
start_time = time.time()

# Load saved model and build the detection function
detect_fn = tf.saved_model.load("saved_model")

end_time = time.time()
elapsed_time = end_time - start_time
print('Done! Took {} seconds'.format(elapsed_time))

Loading model...Done! Took 43.729407787323 seconds


import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings

for image_path in IMAGE_PATHS:

    print('Running inference for {}... '.format(image_path), end='')

    image_np = load_image_into_numpy_array(image_path)

    # Things to try:
    # Flip horizontally
    # image_np = np.fliplr(image_np).copy()

    # Convert image to grayscale
    # image_np = np.tile(
    #     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)

    # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
    input_tensor = tf.convert_to_tensor(image_np)
    # The model expects a batch of images, so add an axis with `tf.newaxis`.
    input_tensor = input_tensor[tf.newaxis, ...]

    # input_tensor = np.expand_dims(image_np, 0)
    detections = detect_fn(input_tensor)

    # All outputs are batches tensors.
    # Convert to numpy arrays, and take index [0] to remove the batch dimension.
    # We're only interested in the first num_detections.
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy()
                   for key, value in detections.items()}
    detections['num_detections'] = num_detections

    # detection_classes should be ints.
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
          image_np_with_detections,
          detections['detection_boxes'],
          detections['detection_classes'],
          detections['detection_scores'],
          category_index,
          use_normalized_coordinates=True,
          max_boxes_to_draw=200,
          min_score_thresh=.30,
          agnostic_mode=False)

    plt.figure()
    plt.imshow(image_np_with_detections)
    print('Done')
plt.show()

Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00008.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00009.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet HHR SS 2010/00010.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Buick Verano Sedan 2012/00011.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Freestar Minivan 2007/00012.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford E-Series Wagon Van 2012/00013.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Elantra Sedan 2007/00014.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00015.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00016.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover Range Rover SUV 2012/00017.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00018.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S5 Coupe 2012/00019.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00020.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Aventador Coupe 2012/00021.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00022.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler PT Cruiser Convertible 2008/00023.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Mustang Convertible 2007/00024.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00025.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00026.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00027.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo XC90 SUV 2007/00028.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Club Cab 2007/00029.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TSX Sedan 2012/00030.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00031.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Grand Cherokee SUV 2012/00032.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00033.jpg... Done
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental Flying Spur Sedan 2007/00034.jpg...

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-80-5f4603fe6f9f> in <module>()
     25 
     26     # input_tensor = np.expand_dims(image_np, 0)
---> 27     detections = detect_fn(input_tensor)
     28 
     29     # All outputs are batches tensors.

/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in _call_attribute(instance, *args, **kwargs)
    699 
    700 def _call_attribute(instance, *args, **kwargs):
--> 701   return instance.__call__(*args, **kwargs)
    702 
    703 

/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs)
    151     except Exception as e:
    152       filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153       raise e.with_traceback(filtered_tb) from None
    154     finally:
    155       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _convert_inputs_to_signature(inputs, input_signature, flat_input_signature)
   2980       flat_input_signature,
   2981       flatten_inputs)):
-> 2982     raise ValueError("Python inputs incompatible with input_signature:\n"
   2983                      f"{format_error_message(inputs, input_signature)}.")
   2984 

ValueError: Python inputs incompatible with input_signature:
  inputs: (
    tf.Tensor(
[[[ 87  87  87 ... 151 151 151]
  [ 86  85  85 ... 152 152 152]
  [ 82  82  82 ... 152 152 152]
  ...
  [133 133 133 ... 133 133 133]
  [133 133 133 ... 133 133 133]
  [133 133 133 ... 133 133 133]]], shape=(1, 400, 600), dtype=uint8))
  input_signature: (
    TensorSpec(shape=(1, None, None, 3), dtype=tf.uint8, name='input_tensor')).


!pwd

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd


%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd


%cd models

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models

!ls

my_rcnn_imagenet  my_ssd_mobilenet


%cd my_ssd_mobilenet/

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models/my_ssd_mobilenet

!ls

checkpoint		     ckpt-24.data-00000-of-00001
ckpt-20.data-00000-of-00001  ckpt-24.index
ckpt-20.index		     ckpt-25.data-00000-of-00001
ckpt-21.data-00000-of-00001  ckpt-25.index
ckpt-21.index		     ckpt-26.data-00000-of-00001
ckpt-22.data-00000-of-00001  ckpt-26.index
ckpt-22.index		     eval
ckpt-23.data-00000-of-00001  pipeline.config
ckpt-23.index		     train


%cd ..

/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models


%reload_ext tensorboard


%tensorboard --logdir my_ssd_mobilenet


%reload_ext tensorboard


tensorboard --logdir my_rcnn_imagenet


%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo


%cd yolov5

/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5

%ls

CONTRIBUTING.md  export.py   __pycache__/      setup.cfg       val.py
data/            hubconf.py  README.md         train.py        yolov5s.pt
detect.py        LICENSE     requirements.txt  tutorial.ipynb
Dockerfile       models/     runs/             utils/


%reload_ext tensorboard


%tensorboard --logdir runs

	filepath	class_name	Bbox
4299	/content/drive/My Drive/capstone/Dataset/Car I...	Chrysler Aspen SUV 2009	[16.0, 29.0, 241.0, 155.0]
2723	/content/drive/My Drive/capstone/Dataset/Car I...	Ford Freestar Minivan 2007	[38.0, 18.0, 192.0, 110.0]
4127	/content/drive/My Drive/capstone/Dataset/Car I...	Volkswagen Beetle Hatchback 2012	[71.0, 65.0, 579.0, 396.0]
1381	/content/drive/My Drive/capstone/Dataset/Car I...	Cadillac Escalade EXT Crew Cab 2007	[3.0, 21.0, 285.0, 195.0]
1800	/content/drive/My Drive/capstone/Dataset/Car I...	Dodge Durango SUV 2007	[15.0, 25.0, 239.0, 182.0]
8005	/content/drive/My Drive/capstone/Dataset/Car I...	Hyundai Azera Sedan 2012	[30.0, 146.0, 666.0, 378.0]
4619	/content/drive/My Drive/capstone/Dataset/Car I...	Chrysler PT Cruiser Convertible 2008	[30.0, 183.0, 503.0, 383.0]
6013	/content/drive/My Drive/capstone/Dataset/Car I...	FIAT 500 Convertible 2012	[274.0, 376.0, 1120.0, 826.0]
1827	/content/drive/My Drive/capstone/Dataset/Car I...	Buick Verano Sedan 2012	[70.0, 105.0, 443.0, 285.0]
1193	/content/drive/My Drive/capstone/Dataset/Car I...	Geo Metro Convertible 1993	[62.0, 141.0, 309.0, 246.0]

Capstone project - CV2 - Cars Object detection¶

Summary of problem statement, data and findings¶

SSD Mobilenet based object detection algorithm¶

Faster RCNN Object Detection model¶

Yolo algorithm for object detection¶

Object detection with different models:¶

	Car Name	Car_Type	Model	Year
19	Audi S6 Sedan 2011	Sedan	Audi	2011
124	HUMMER H2 SUT Crew Cab 2009	Cab	HUMMER	2009
40	Bentley Mulsanne Sedan 2011	Sedan	Bentley	2011
155	Lincoln Town Car Sedan 2011	Sedan	Lincoln	2011
100	Ferrari FF Coupe 2012	Coupe	Ferrari	2012
33	BMW M3 Coupe 2012	Coupe	BMW	2012
143	Jaguar XK XKR 2012	XKR	Jaguar	2012
133	Hyundai Sonata Hybrid Sedan 2012	Sedan	Hyundai	2012
91	Dodge Magnum Wagon 2008	Wagon	Dodge	2008
181	Suzuki Kizashi Sedan 2012	Sedan	Suzuki	2012

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
0	00001.jpg	600	400	14	39.0	569.0	116.0	375.0	Audi TTS Coupe 2012	/content/drive/My Drive/capstone/Dataset/Car I...
1	00002.jpg	900	675	3	36.0	868.0	116.0	587.0	Acura TL Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
2	00003.jpg	640	480	91	85.0	601.0	109.0	381.0	Dodge Dakota Club Cab 2007	/content/drive/My Drive/capstone/Dataset/Car I...
3	00004.jpg	2100	1386	134	621.0	1484.0	393.0	1096.0	Hyundai Sonata Hybrid Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
4	00005.jpg	144	108	106	14.0	133.0	36.0	99.0	Ford F-450 Super Duty Crew Cab 2012	/content/drive/My Drive/capstone/Dataset/Car I...
...	...	...	...	...	...	...	...	...	...	...
8139	08140.jpg	424	360	78	3.0	423.0	44.0	336.0	Chrysler Town and Country Minivan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
8140	08141.jpg	800	600	196	138.0	706.0	150.0	523.0	smart fortwo Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...
8141	08142.jpg	750	469	163	26.0	660.0	246.0	449.0	Mercedes-Benz SL-Class Coupe 2009	/content/drive/My Drive/capstone/Dataset/Car I...
8142	08143.jpg	1600	1067	112	78.0	1489.0	526.0	908.0	Ford GT Coupe 2006	/content/drive/My Drive/capstone/Dataset/Car I...
8143	08144.jpg	883	683	17	20.0	862.0	240.0	677.0	Audi 100 Sedan 1994	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
3249	03250.jpg	640	426	176	366.0	602.0	163.0	301.0	Rolls-Royce Ghost Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
1072	01073.jpg	555	370	24	8.0	545.0	63.0	314.0	Audi S4 Sedan 2007	/content/drive/My Drive/capstone/Dataset/Car I...
2060	02061.jpg	688	456	183	43.0	646.0	110.0	427.0	Suzuki SX4 Hatchback 2012	/content/drive/My Drive/capstone/Dataset/Car I...
553	00554.jpg	320	240	138	41.0	299.0	38.0	190.0	Hyundai Sonata Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
4512	04513.jpg	400	250	110	21.0	382.0	31.0	228.0	Ford Edge SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
2566	02567.jpg	495	223	152	42.0	471.0	65.0	197.0	Lamborghini Gallardo LP 570-4 Superleggera 2012	/content/drive/My Drive/capstone/Dataset/Car I...
7898	07899.jpg	300	225	148	28.0	282.0	18.0	183.0	Jeep Grand Cherokee SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
3337	03338.jpg	610	404	103	36.0	569.0	135.0	365.0	Ferrari 458 Italia Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...
1990	01991.jpg	627	448	135	94.0	530.0	158.0	421.0	Hyundai Elantra Sedan 2007	/content/drive/My Drive/capstone/Dataset/Car I...
3732	03733.jpg	1024	768	51	70.0	936.0	120.0	619.0	Cadillac CTS-V Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
6002	07010.jpg	560	373	120	118.0	485.0	112.0	351.0	GMC Yukon Hybrid SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
2154	05689.jpg	655	261	132	27.0	509.0	8.0	255.0	Hyundai Tucson SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
6194	01758.jpg	640	480	114	1.0	639.0	103.0	480.0	Ford F-150 Regular Cab 2007	/content/drive/My Drive/capstone/Dataset/Car I...
2134	05412.jpg	434	326	83	57.0	378.0	114.0	253.0	Dodge Caliber Wagon 2012	/content/drive/My Drive/capstone/Dataset/Car I...
758	03018.jpg	640	480	117	43.0	559.0	110.0	452.0	Ford Fiesta Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
717	00478.jpg	112	84	174	9.0	98.0	7.0	81.0	Ram C-V Cargo Van Minivan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
238	01456.jpg	500	375	120	35.0	464.0	63.0	331.0	GMC Yukon Hybrid SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
396	01316.jpg	429	262	57	20.0	406.0	35.0	236.0	Chevrolet Corvette Ron Fellows Edition Z06 2007	/content/drive/My Drive/capstone/Dataset/Car I...
1558	02910.jpg	300	165	56	38.0	250.0	35.0	145.0	Chevrolet Corvette ZR1 2012	/content/drive/My Drive/capstone/Dataset/Car I...
259	04408.jpg	640	480	75	27.0	626.0	135.0	398.0	Chevrolet Silverado 1500 Regular Cab 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
3803	04961.jpg	750	469	154	84.0	686.0	89.0	424.0	Land Rover Range Rover SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
2638	05312.jpg	480	320	45	154.0	324.0	125.0	286.0	Bugatti Veyron 16.4 Convertible 2009	/content/drive/My Drive/capstone/Dataset/Car I...
1981	04069.jpg	300	225	84	22.0	285.0	55.0	199.0	Dodge Caliber Wagon 2007	/content/drive/My Drive/capstone/Dataset/Car I...
1888	00738.jpg	640	480	55	42.0	602.0	153.0	389.0	Chevrolet Corvette Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...
5997	05517.jpg	450	241	21	47.0	416.0	16.0	198.0	Audi S5 Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
0	06890.jpg	1600	1200	108	196.0	1372.0	43.0	1156.0	Ford Freestar Minivan 2007	/content/drive/My Drive/capstone/Dataset/Car I...
1	03838.jpg	259	194	14	13.0	251.0	30.0	156.0	Audi TTS Coupe 2012	/content/drive/My Drive/capstone/Dataset/Car I...
2	03741.jpg	300	180	100	21.0	263.0	4.0	150.0	FIAT 500 Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...
3	05159.jpg	600	450	114	9.0	600.0	66.0	373.0	Ford F-150 Regular Cab 2007	/content/drive/My Drive/capstone/Dataset/Car I...
4	03349.jpg	650	460	25	26.0	632.0	104.0	392.0	Audi TT RS Coupe 2012	/content/drive/My Drive/capstone/Dataset/Car I...
...	...	...	...	...	...	...	...	...	...	...
6510	06603.jpg	628	405	7	39.0	595.0	127.0	360.0	Acura ZDX Hatchback 2012	/content/drive/My Drive/capstone/Dataset/Car I...
6511	03612.jpg	438	329	69	80.0	350.0	40.0	283.0	Chevrolet Silverado 2500HD Regular Cab 2012	/content/drive/My Drive/capstone/Dataset/Car I...
6512	00754.jpg	625	468	95	60.0	578.0	2.0	440.0	Dodge Durango SUV 2007	/content/drive/My Drive/capstone/Dataset/Car I...
6513	08125.jpg	318	180	55	24.0	225.0	85.0	159.0	Chevrolet Corvette Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...
6514	07391.jpg	320	240	146	21.0	319.0	36.0	198.0	Jeep Wrangler SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	filename	width	height	class	xmin	xmax	ymin	ymax	class_name	filepath
0	04506.jpg	1600	1200	63	478.0	1534.0	286.0	979.0	Chevrolet Sonic Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
1	04600.jpg	259	194	190	17.0	241.0	47.0	158.0	Volkswagen Golf Hatchback 2012	/content/drive/My Drive/capstone/Dataset/Car I...
2	04462.jpg	640	480	85	9.0	617.0	26.0	445.0	Dodge Caravan Minivan 1997	/content/drive/My Drive/capstone/Dataset/Car I...
3	02906.jpg	640	480	49	54.0	603.0	77.0	385.0	Buick Verano Sedan 2012	/content/drive/My Drive/capstone/Dataset/Car I...
4	05184.jpg	630	418	83	24.0	607.0	144.0	398.0	Dodge Caliber Wagon 2012	/content/drive/My Drive/capstone/Dataset/Car I...
...	...	...	...	...	...	...	...	...	...	...
1624	02584.jpg	480	360	12	33.0	458.0	48.0	289.0	Audi RS 4 Convertible 2008	/content/drive/My Drive/capstone/Dataset/Car I...
1625	01438.jpg	800	600	33	16.0	768.0	132.0	574.0	BMW X6 SUV 2012	/content/drive/My Drive/capstone/Dataset/Car I...
1626	03655.jpg	1024	768	43	72.0	992.0	101.0	648.0	Bentley Continental GT Coupe 2007	/content/drive/My Drive/capstone/Dataset/Car I...
1627	06440.jpg	485	323	181	18.0	468.0	135.0	314.0	Suzuki Aerio Sedan 2007	/content/drive/My Drive/capstone/Dataset/Car I...
1628	02737.jpg	600	450	196	50.0	558.0	72.0	419.0	smart fortwo Convertible 2012	/content/drive/My Drive/capstone/Dataset/Car I...

	Car Name
0	AM General Hummer SUV 2000
1	Acura RL Sedan 2012
2	Acura TL Sedan 2012
3	Acura TL Type-S 2008
4	Acura TSX Sedan 2012
...	...
191	Volkswagen Beetle Hatchback 2012
192	Volvo C30 Hatchback 2012
193	Volvo 240 Sedan 1993
194	Volvo XC90 SUV 2007
195	smart fortwo Convertible 2012